research article gene expression music algorithm-based

Research ArticleGene Expression Music Algorithm-Based Characterizationof the Ewing Sarcoma Stem Cell Signature

Martin Sebastian Staege

University Clinic and Polyclinic for Child and Adolescent Medicine, Martin Luther University of Halle-Wittenberg,06120 Halle (Saale), Germany

Correspondence should be addressed to Martin Sebastian Staege; [email protected]

Received 8 April 2016; Accepted 15 June 2016

Academic Editor: Rene Rodrıguez

Copyright © 2016 Martin Sebastian Staege. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Gene ExpressionMusic Algorithm (GEMusicA) is a method for the transformation of DNAmicroarray data into melodies that canbe used for the characterization of differentially expressed genes. Using this method we compared gene expression profiles fromendothelial cells (EC), hematopoietic stem cells, neuronal stem cells, embryonic stem cells (ESC), and mesenchymal stem cells(MSC) and defined a set of genes that can discriminate between the different stem cell types. We analyzed the behavior of publicmicroarray data sets from Ewing sarcoma (“Ewing family tumors,” EFT) cell lines and biopsies in GEMusicA after prefilteringDNA microarray data for the probe sets from the stem cell signature. Our results demonstrate that individual Ewing sarcoma celllines have a high similarity to ESC or EC. Ewing sarcoma cell lines with inhibited Ewing sarcoma breakpoint region 1-Friendleukemia virus integration 1 (EWSR1-FLI1) oncogene retained the similarity to ESC and EC. However, correlation coefficientsbetween GEMusicA-processed expression data between EFT and ESC decreased whereas correlation coefficients between EFTand EC as well as between EFT and MSC increased after knockdown of EWSR1-FLI1. Our data support the concept of EFT beingderived from cells with features of embryonic and endothelial cells.

1. Introduction

The stem cell phenotype of cancer cells can be the conse-quence of the malignant transformation that led to de novoacquisition of a stem cell-like phenotype or this phenotypecan be reminiscent of a normal stem cell that serves as the cellof origin for the cancer cells. In both cases the gene expressionprofile of the cancer cells will show similarities to the geneexpression profile of stem cells. Characterization of this stemcell signature can be useful for the identification of new targetstructures and might also give hints about the histogeneticorigin of cancer cells in cases where the cell of origin has notbeen identified.

Ewing sarcoma (or the “Ewing family of tumors,” EFT) isan interesting model for a tumor entity with uncertain cell oforigin thatmight be derived from stem cells. Gene expressiondata suggest a relationship between EFT and endothelial cells,

neuroectodermal cells, or mesenchymal stem cells [1–4].The majority of EFT carry chromosomal translocations lead-ing to gene fusions between members of the TET (translo-cated in liposarcoma, Ewing sarcoma breakpoint region1, TATA box binding protein-associated factor) family ofRNA binding proteins and the ETS (avian erythroblastosisvirus E26 oncogene homolog) family of transcription factors(reviewed in [5]). In most cases, the TET family memberEWSR1 (Ewing sarcoma breakpoint region 1) is fused to theETS family member FLI1 (Friend leukemia virus integration1). Ewing proposed that EFT are of endothelial origin [6].Afterwards, a neuroectodermal origin was suggested by theobservation of neuronal marker expression in EFT. Indeed,expression of the EFT specific EWSR1-FLI1 oncogene inneuroblastoma cells can induce an EFT-like phenotype [7].However, expression of the oncogene in nonneural cells caninduce expression of neuronal markers, suggesting that the

Hindawi Publishing CorporationStem Cells InternationalVolume 2016, Article ID 7674824, 10 pageshttp://dx.doi.org/10.1155/2016/7674824

2 Stem Cells International

neuronal phenotype might be partially a consequence ofoncogene expression [8]. In addition to neuroectodermalcells, mesenchymal stem cells (MSC) have been discussedas cells of origin for EFT [1, 2, 9–11]. However, the geneexpression profile of EWSR1-FLI1 transgenic MSC is notcompletely identical to the gene expression profile of EFT.MSC are a heterogeneous population of stem cells and theactivity of TET-ETS oncofusion proteins is influenced by thehost cell type [12, 13].Therefore, it seems possible that the finalphenotype of EFT cells is influenced not only by the TET-ETSfusion type but also by the affected stem cell subpopulation.

Recently, we demonstrated that the transformation ofgene expression data into melodies can be used for the“musical” analysis of these data and that the Gene ExpressionMusic Algorithm (GEMusicA) allows the discriminationbetween samples with different biological behavior [14]. Forinstance, GEMusicA can be used for the discriminationbetween different tumor entities or for the discriminationbetween tumor cells and their normal counterparts [14].GEMusicA is an alternative method to more conventionalmethods of microarray data analysis. The outputs of GEMu-siA analyses are sound files as well as the correspondingmusical scores which can be used for visual presentation ofthe data. Alternatively, the sound files can be used directlyfor acoustical data presentation. GEMuiscA preferentiallyenriches probe sets with high signal intensities which aremore likely to have a high impact on the phenotype of a cell[14]. The generated melodies are highly specific for the indi-vidual samples and high-pitched notes directly indicate geneswith high expression in these samples. GEMusicA includesa function for the unsupervised selection of differentiallyexpressed genes on the basis of the variance. In the presentpaper we used this approach for the definition of a stem cellsignature and tested the behavior of this signature in EFTmicroarray data.

2. Materials and Methods

2.1. Microarray Data Sets. All microarray data sets weredownloaded from the Gene Expression Omnibus (GEO)database [15].The following data sets were used: GSE1824 [1],GSE1825 [2], GSE7007 [3], GSE2248 [16], GSE2361 [17],GSM139881–GSM139883 and GSM139888–GSM139893 fromGSE6029 [18], GSM86779, GSM86781 and GSM867783 formGSE3788 [19], GSE2638 [20], GSE2639 [20], and GSM1529-859–GSM1529861 from GSE62600 [21].

2.2. GEMusicA Analysis. Cel files were processed with theGEMusicAR script [14]. In a first step, cel files from hema-topoietic stem cells (HSC) [19], embryonic stem cells (ESC)[16], neuronal stem cells (NSC) [21], and different subtypes ofmesenchymal stem cells (MSC) [3, 16, 18] were RMA (RobustMultiarray Average) normalized and used for selection ofprobe sets with differential expression between differentstem cell types. The original GEMusicAR script [14] usesthe RMA algorithm only in combination with AffymetrixExon arrays. In order to allow usage with the Affymetrix

HG U133A microarrays in this paper, the argument “level”in the function “ProcessCelRMA” was deleted. The numberof probe sets that were used for generation of melodieswas set to 1114 (5% of the total number of probe sets onthe used arrays). The 1114 probe sets that were filtered byGEMusicA in this way were used to generate a prefilteredprobe set list that was used in the following steps for analysisof tumor samples. For these analyses, the cel files from stemcells were combined with the required tumor samples ornormal tissue samples and RMA normalized. Subsequenttransformations of signal intensities into frequencies wereperformed by using the complete number of prefilteredprobe sets (𝑁 = 1114). Music scores were prepared fromthe automatically generated TeX documents as described[14].

3. Results and Discussion

GEMusicA generates melodies from DNA microarray data.The algorithm includes a procedure that filters probe setswith high variance of the signal intensities. These probe setsare likely to have a higher information content than probesets with low variability [14]. We used this approach for thecharacterization of tumor specific gene expression profilesanddemonstrated that the generatedmelodies can be used fordiscrimination between different tumor entities, for example,neuroblastoma and Ewing sarcoma cell lines [14]. We askedwhether this method can be used for the definition of geneexpression signatures that are specific for certain stem cellpopulations. For this end we combinedmicroarray data fromembryonic stem cells, neuronal stem cells, hematopoieticstem cells, and different types of mesenchymal stem cellsand used GEMusicA for the generation of melodies. Asshown in Figure 1, the transformed signal intensities fromthe GEMusicA-filtered probe sets allow clear discriminationbetween the different stem cell types. Figure 2 shows the first15 tones from the melodies from representative samples. Thecorresponding genes include several well-known stem cellspecific candidates. For example, embryonic stem cells arecharacterized by a high frequency of the tones representingLINE-1 type transposase domain containing 1 (L1TD1) or Lin-28 homolog A (LIN28A) that are both known to be markersfor ESC [22, 23]. Similarly, prominin 1 (PROM1 = CD133)which is a marker for HSC [24] and NSC [25] is presentedby high-pitched tones in the melodies from these two stemcell types (Figure 2).

In a next step we included cell lines from Ewing sarcomaand neuroblastoma [1, 3] in the analysis (Figure 3). All EFTcell lines are characterized by EWS-FLI1 type 1 fusion tran-scripts. The neuroblastoma cell lines CHP-126 and SiMa arecell lines with a MYCN (v-myc avian myelocytomatosis viraloncogene, neuroblastoma derived) amplification whereasSH-SY5Y cells have noMYCN amplification. Neuroblastoma(NB) cell lines showed the expected high similarity to NSC.Interestingly, all EFT cell lines showed a higher similarityto NSC and ESC than to MSC (Figure 3). Cell lines SK-N-MC (established from a supraorbital metastasis of an Askintumor) and EW24 (established from a bone tumor) formed

Stem Cells International 3

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Figure 1: GEMusicA can discriminate between different stem cell types. DNA microarray cel files from embryonic stem cells (ESC) [16],neuronal stem cells (NSC) [21], hematopoietic stem cells (HSC) [19], bone marrow derived mesenchymal stem cells (BM-MSC) [3, 16, 18],two types of umbilical cord blood derived mesenchymal stem cells (UCB1-MSC, UCB2-MSC) [18], and mesenchymal stem cells derivedfrom embryonic stem cells (ESCd-MSC) [16] were RMA-normalized and used for transformation into melodies using the GEMusicAimplementation in R. The following parameters were used: minfreq = 27.5, tonesteps = 12, numkeys = 88, mindur = 8, vol = 4,TeXscalefactor = 1, maxNdots = 2, and 𝑁 = 1114. Presented is a cluster analysis of the GEMusicA-processed 1114 probe sets (Manhattandistance, complete linkage clustering).


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even a cluster together with ESC, suggesting that the embry-onic phenotype in these cell lines is more pronounced than inA673 cells (established from a muscle tumor). A relationshipof EFT and endothelial cells has been considered for a longtime [1, 4, 6]. Therefore, we asked how endothelial cells willbehave in this analysis and we included a set of endothelialcells in our data set. As shown in Figure 4, the rough topologyof the clustering tree remains unchanged. Interestingly, EFTcell line A673 formed a cluster together with the endothelialcells in this analysis whereas the other EFT cell line samplesremained in the cluster with ESC. A673 cells have been shownto have endothelial differentiation capacity under certain

conditions. After inhibition of enhancer of zeste homologue2 or other components of the polycomb repressive complex2 (PRC2), A673 cells start tube formation in matrigel assays[4]. The results from our GEMusicA analysis support theendothelial features of this cell line. After extension of thedata set with native tumor biopsies fromNB and EFT patients[1, 3] as well as a panel of normal tissues (normal body atlas,NBA [17]), we observed again the high similarity betweenA673 cells and endothelial cells (Figure 5). EFT biopsiesclustered together with SK-N-MC cells, EW24 cells, NBcell lines, NSC, and ESC. NB biopsies showed a differentbehavior in this cluster analysis, indicating that the behavior


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Figure 3:The stem cell signature of Ewing sarcoma cells. DNAmicroarray cel files from stem cells were processed as described in the captionof Figure 1. DNA microarray data from these stem cells were combined with data from a panel of neuroblastoma cell lines (NB) [1] and theEwing sarcoma cell lines A673, EW24, and SK-N-MC [1, 3]. After RMA normalization, transformation into melodies was performed usingthe GEMusicA implementation in R. For this end, only the probe sets from the prefiltered stem cell signature from Figure 1 were used. Thefollowing parameters were used: minfreq = 27.5, tonesteps = 12, numkeys = 88, mindur = 8, vol = 4, TeXscalefactor = 1, maxNdots = 2,and𝑁 = 1114. Presented is a cluster analysis of the GEMusicA-processed 1114 probe sets (Manhattan distance, complete linkage clustering).

of the EFT samples is specific for this tumor entity andnot a general phenomenon for (small round blue cell)tumors. Four of five NB biopsies clustered together with fetalbrain. The NB cell lines remained stable in the cluster withNSC.

Thehigh similarity betweenEFT cell lines and endothelialcells (A673) or ESC (EW24, SK-NM-C) can be a hint forhistogenetic origin or a consequence of oncogene activation.

The EFT cell line data set from GSE7007 contains data fromEFT cell lines after knockdown of EWSR1-FLI1 [3]. In thecluster analyses (Figures 3–5), EFT cell lines after knockdownof EWSR1-FLI1 and control cells clustered together, suggest-ing that EWSR1-FLI1 has only small impact on the expressionof the genes in the defined stem cell signature. We com-pared the correlation coefficients between different non-EFTsamples and EFT cell lines with and without knockdown of

6 Stem Cells InternationalG

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Figure 4: Similarities in the stem cell signature between Ewing sarcoma cells and endothelial cells. DNA microarray cel files from stemcells were processed as described in the caption of Figure 1. DNA microarray data from these stem cells were combined with a panel ofmicrovasculature endothelial cells (HMEC) [20], macrovasculature endothelial cells (HUVEC) [20], neuroblastoma cell lines (NB) [1], andthe Ewing sarcoma cell lines A673, EW24, and SK-N-MC [1, 3]. After RMAnormalization, transformation intomelodies was performed usingthe GEMusicA implementation in R. For this end, only the probe sets from the prefiltered stem cell signature from Figure 1 were used. Thefollowing parameters were used: minfreq = 27.5, tonesteps = 12, numkeys = 88, mindur = 8, vol = 4, TeXscalefactor = 1, maxNdots = 2,and𝑁 = 1114. Presented is a cluster analysis of the GEMusicA-processed probe sets (Manhattan distance, complete linkage clustering).

EWSR1-FLI1 (Figure 6). Interestingly, the correlation coef-ficients for ESC, NSC, HSC, or NB decreased after knock-down of EWSR1-FLI1 (𝑝 < 0.01) whereas the correlationcoefficients for MSC or endothelial cells increased afterknockdown of EWSR1-FLI1 (𝑝 < 0.00001). This incre-ment in the correlation coefficient is based on changesin signal intensities for probe sets that can discriminatebetween MSC/endothelial cells and other stem cells. Oneexample is shown in Figure 7. Dickkopf homolog 1 (DKK1) is

upregulated in EWSR1-FLI1 inhibited EFT cells. DKK1 ishighly expressed in MSC but also in endothelial cells asindicated by the high-pitched tones representing DKK1 (Fig-ure 7). Downregulation of DKK1 and upregulation of DKK2after transgenic expression of EWSR1-FLI1 inMSC have beendescribed [26]. DKK2 which is an Ewing sarcoma specificgene [1] and is upregulated by EWSR1-FLI1 is not included inthe prefiltered stem cell signature probe sets and, therefore,not included in the melodies. The decreased correlation


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(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)(EFT)

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Figure 5: The stem cell signature of Ewing sarcoma biopsies. DNA microarray cel files from stem cells were processed as described in thecaption of Figure 1. DNA microarray data from these stem cells were combined with a panel of microvasculature endothelial cells (HMEC)[20], microvasculature endothelial cells (HUVEC) [20], neuroblastoma cell lines (NBcl) [1], Ewing sarcoma cell lines A673, EW24, and SK-N-MC (EFTcl) [1, 3], neuroblastoma biopsies (NB) [2], Ewing sarcoma biopsies (EFT) [2, 3], and a panel of normal tissues of varying origin(NBA) [17]. Transformation intomelodies was performed using theGEMusicA implementation inR. For this end, only the probe sets from theprefiltered stem cell signature from Figure 1 were used. The following parameters were used: minfreq = 27.5, tonesteps = 12, numkeys = 88,mindur = 8, vol = 4, TeXscalefactor = 1, maxNdots = 2, and 𝑁 = 1114. Presented is a cluster analysis of the GEMusicA-processed probesets (Manhattan distance, complete linkage clustering).


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0.60

ControlEWSR1-FLI1 knockdown

𝜌/𝜌

0

Figure 6: Relative Pearson correlation coefficients from the data from Figure 5. DNAmicroarray cel files were processed as described in thecaption of Figure 5. Pearson correlation coefficients 𝜌 were calculated for samples from Ewing sarcoma cell lines from data set GSE7007 [3]and all stem cell samples. For each EFT sample, the mean of the Pearson correlation coefficients in the control samples without EWSR1-FLI1inhibition 𝜌

0was set as 1. Presented aremeans and standard deviations for the correlations between the indicated cell types and Ewing sarcoma

cell lines after knockdown of EWSR1-FLI1 (EWS-FLI1 knockdown) or control cell lines (control) without EWSR1-FLI1 knockdown.

coefficient between EFT and ESC after knockdown ofEWSR1-FLI1 suggests that the expression of TET-ETS onco-genes in MSC can induce the expression of an ESC-likephenotype.

An interesting aspect of the presented GEMusicA anal-yses is the observation that different EFT cell lines behavedifferently. Whereas EW24 cells and SK-N-MC cells demon-strate a higher similarity to ESC, A673 cells show a veryhigh similarity to endothelial cells (and MSC). In this regardit is interesting to note that A673 cells have been initiallyestablished as rhabdomyosarcoma cells [27] whereas SK-N-MC cells were established as neuroblastoma cell line [28],suggesting that the histological phenotype of EFT can varyextensively. Evidence for the classification of both cell linesas EFT comes from the detection of EWSR1-FLI1 fusions.Taking into account the fact that EWSR1-FLI1 can induceEFT-like gene expression profiles in different other cell types,it seems possible that different cells of origin can giveraise to EFT. On the other hand, TET-ETS translocationsare the sole cytogenetic aberration in only approximatelyone-fourth of EFT (data from the Mitelman Database ofChromosome Aberrations and Gene Fusions in Cancer,http://cgap.nci.nih.gov/Chromosomes/Mitelman). The largemajority of tumors show additional alterations and it seemslikely that at the molecular level 100% of the tumors will har-bor “secondary” alterations. Therefore, it seems evident thatTET-ETS fusions alone are not sufficient for the developmentof EFT and that additional events are required. These eventsmight vary between different tumors andmight be one reasonfor the heterogeneity of EFT cell lines.The histogenetic origin

of EFT is a miracle for nearly a century. At diagnosis, thetumor cells have a history of in vivo growth and evolutionthat had molded the phenotype and gene expression of thetumor cells. These effects are at least partially independentof the TET-ETS oncogene. Therefore, ectopic expression ofTET-ETS oncogenes in normal cells will not result in theexact EFT phenotype. On the other hand, manipulation ofTET-ETS expression in EFTwill likely not result in a cell withthe exact phenotype of the cell of origin. From our data wecannot draw the conclusion that MSC are optimal candidatesfor EFT mother cells. Endothelial cells (or probably anendothelial precursor cell population) should be consideredas an alternative source at least for a subpopulation ofEFT.

4. Conclusions

Our data demonstrate that GEMusicA is feasible for thecharacterization of stem cell-type specific gene expressionsignatures. The comparison of GEMusicA-processed DNAmicroarray melodies from EFT and stem cells supports theconcept of an endothelial and embryonic phenotype of theEFT mother cell.

Competing Interests

The author declares that he has no competing interests.


A673 control

EW24 control

SK-N-MC control

ESC

CHP-126 (NB)

NSC

HSC

A673 knockdown

EW24 knockdown

SK-N-MC knockdown

BM-MSC

UCB2-MSC

UCB1-MSC

HUVEC

HMEC

ESCd-MSC

SELL

DKK

1

LEFT

Y1D

CN

DLK

1

SELL

DKK

1

LEFT

Y1D

CN

DLK

1

Figure 7: DKK1 expression discriminates betweenMSC/EC and other cell types. DNAmicroarray cel files were processed as described in thecaption of Figure 5. Presented is the 10th bar from representative individual samples. The differentially expressed gene DKK1 is highlighted.

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