genomicsscreenintransformedstemcellsreveals...

Genomics screen in transformed stem cells revealsRNASEH2A, PPAP2C, and ADARB1 as putativeanticancer drug targets

James M. Flanagan,1 Juan M. Funes,1

Stephen Henderson,1 Laurence Wild,1

Nessa Carey,2 and Chris Boshoff1

1UCL Cancer Institute, London, United Kingdom and2CellCentric Ltd., Cambridge, United Kingdom

AbstractSince the sequencing of the human genome, recent effortsin cancer drug target discovery have focused more on theidentification of novel functions of known genes andthe development of more appropriate tumor models. In thepresent study, we investigated in vitro transformedhuman adult mesenchymal stem cells (MSC) to identifynovel candidate cancer drug targets by analyzing thetranscriptional profile of known enzymes compared withnon-transformed MSC. The identified enzymes were com-pared with published cancer gene expression data sets.Surprisingly, the majority of up-regulated enzymes arealready known cancer drug targets or act within knowndruggable pathways. Only three enzymes (RNASEH2A,ADARB1, and PPAP2C) are potentially novel targets thatare up-regulated in transformed MSC and expressed innumerous carcinomas and sarcomas. We confirmed theoverexpression of RNASEH2A, PPAP2C, and ADARB1 intransformed MSC, transformed fibroblasts, and cancercell lines MCF7, SK-LMS1, MG63, and U2OS. In func-tional assays, we show that small interfering RNAknockdown of RNASEH2A inhibits anchorage-indepen-dent growth but does not alter in vitro proliferation ofcancer cell lines, normal MSC, or normal fibroblasts.Knockdown of PPAP2C impaired anchorage-dependentin vitro growth of cancer cell lines and impaired thein vitro growth of primary MSC but not differentiatedhuman fibroblasts. We show that the knockdown of

PPAP2C decreases cell proliferation by delaying entryinto S phase of the cell cycle and is transcriptionallyregulated by p53. These in vitro data validate PPAP2Cand RNASEH2A as putative cancer targets and endorsethis in silico approach for identifying novel candidates.[Mol Cancer Ther 2009;8(1):249–60]

IntroductionAccording to the Food and Drug Administration, the cost ofdevelopment for each new anticancer drug ranges from 800million to 2 billion U.S. dollars (1). It is estimated that onlyone in five drugs that enter phase I trials will reach U.S.regulatory approval; more importantly, half of drugs thatenter the expensive phase III stage will not reach authori-zation (1). The reasons for this high attrition rateare numerous, including issues of on-target and off-targetsafety concerns, particularly failure in the biologicalhypotheses driving the target (2). Much of the recent effortsin target discovery have therefore focused on identificationof novel disease relevant targets with a reduced probabilityof subsequent failure by improving early validationemploying more suitable cancer models (2).There have been numerous attempts of in silico

prediction of cancer drug targets including structuralanalyses of all known proteins to define ‘‘the druggablegenome,’’ which converges on 2,000 to 3,000 targets thatcontain protein folds that are amenable to drug-like smallchemical compounds (3, 4). Statistical approaches havealso been used to define 250 potentially successful cancerrelevant targets of which 60 to 70 are currently beingactively investigated (5). However, the druggable genomeis a ‘‘moving target’’ with protein modeling algorithmsbecoming more sophisticated, more hypothetical proteinsbeing characterized allowing better annotations, and achanging perception of what classes of proteins can betargeted (4). The largest categories of current drug targetsare G protein-coupled receptors, ion channels, andvarious classes of enzymes (e.g., protein kinases; ref. 4).Recent advances, including antisense delivery, small inter-fering RNA (siRNA) knockdown, and small-moleculecarriers, may extend the range of proteins that could betargeted (6, 7).Recent studies have taken a reverse genomics approach

to identify potential drug targets by screening short hair-pin RNA (shRNA) libraries in cancer cell lines and assayingfor genes required for proliferation (8, 9). This approachhas revealed a larger number of genes required by normalcells for proliferation and substantially less in cancercells, reflecting the ability of cancer cells to evade growth-inhibitory cues (9). This work defines ‘‘non-oncogeneaddiction’’ genes, which are required by cancer cells to

Received 7/8/08; revised 9/10/08; accepted 10/3/08.

Grant support: CellCentric (J.M. Flanagan and N. Carey), Medical ResearchCouncil UK (L. Wild), and Cancer Research UK (J.M. Flanagan, S.Henderson, and C. Boshoff).

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

Requests for reprints: James M. Flanagan, Cancer Research UK ViralOncology Group, University College London Cancer Institute, PaulO’Gorman Building, 74 Huntley Street, London, United Kingdom WC1E6BT. Phone: 44-20-7679-6859; Fax: 44-20-7679-6851.E-mail: [email protected]

Copyright C 2009 American Association for Cancer Research.

doi:10.1158/1535-7163.MCT-08-0636

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proliferate but are not required for normal cell proliferation(10).The cancer stem cell hypothesis suggests that tumors

derive from a subpopulation of multipotent stem cells,which continually renew and sustain the malignant growth(11). The tumor stem cells are potentially more resistant tocytotoxic therapies and may be molecularly distinct fromthe bulk of the tumor cells (12). Most cell culture models ofcarcinogenesis have used differentiated cells or well-established cancer cell lines to investigate the propertiesof oncogenes, tumor suppressor genes, or to identify novelcancer drug targets (13). However, if the cancer stem cellhypothesis is correct, then the most appropriate cells inwhich to investigate the genes involved in carcinogenesismay be stem cells. To this end, we have generatedtransformed human adult mesenchymal stem cells (MSC)using a stepwise introduction of five genetic hits includinghTERT, inactivation of p53 and pRb, introduction of smallT antigen to inactivate PP2A leading to stabilization ofc-Myc, and finally introduction of H-Ras (14). We haveshown that MSC require the same number of genetic hits(five) as differentiated cells to become fully transformed(tumor formation in athymic mice).We hypothesize that the enzymes specifically up-

regulated in the transformed MSC model will also beup-regulated in various sarcomas and carcinomas andmay provide novel targets for cancer drug development.In this study, we have taken a forward genomicsapproach for drug target discovery by mining geneexpression data from numerous cancer microarray datasets. Due to the current interest in enzymes for drugtargets, we have focused here on identifying novelenzymes as candidate cancer drug targets. This trans-formed primary stem cell model also provides anexcellent resource for in vitro validation of any identifiedtherapeutic targets or to test novel drugs in the earlystages of stepwise oncogenic transformation.

Materials andMethodsEnzyme Data SetWe identified all known enzymes in the human genome

using the Expasy Enzyme database3 August 21, 2007release, which categorizes human enzymes into 207 oxi-doreductases (EC 1), 319 transferases (EC 2), 300 hydrolases(EC 3), 63 lyases (EC 4), 33 isomerases (EC 5), and 73 ligases(EC 6). These f1,000 enzymes are represented by 2,256probe sets from the Affymetrix hgu133plus2 genechips. Forthis study, we have not included candidate enzymes thatdo not have an EC number.

Gene ExpressionMicroarray DataWe have used our transformed MSC cancer model Gene

Expression Microarray data as a primary screen for up-regulation of enzymes during transformation using acutoff of 2-fold up-regulation (P < 0.05; ref. 14). Extensive

quantitative reverse transcription-PCR (qRT-PCR) valida-tion of the data set has been done previously, and furtherdetails and quality-control measures can be found in theArrayExpress database4 with the accession no. E-MEXP-563 (14).The carcinoma expression data were downloaded from

the expO public repository5 by way of the National Centerfor Biotechnology Information Gene Expression Omnibusdatabase.6 We selected 146 samples hybridized to Affy-metrix hgu133plus2 genechips, covering 11 common typesof carcinoma (breast, colon, endometrium, kidney clear-cell carcinoma, kidney papillary carcinoma, lung adeno-carcinoma, ovary, prostate, rectum, thyroid, and bladder)with no fewer than 10 of each type. Our mesenchymaltumor expression data represent 19 different subtypes,including alveolar rhabdomyosarcoma, chondroblas-toma, chondrosarcoma, chordoma, chondromyxoidfibroma, dedifferentiated chondrosarcoma, embryonalrhabdomyosarcoma, Ewing’s sarcoma, fibromatosis,lipoma, leiomyosarcoma, myxoid liposarcoma, synovialsarcoma, malignant peripheral nerve sheath tumor,neurofibroma, osteosarcoma, pleiomorphic sarcoma,schwannoma, and well-differentiated liposarcoma (15).Raw CEL file data were processed and normalized toproduce expression profiles using the ‘‘rma’’ algorithm.Cancer versus normal tissue control gene expression datawere identified employing the Oncomine Web resource7

using the differential expression function with a P cutoffof <0.0001 (Supplementary Table).8 Oncomine uses a dataanalysis method, whereby all data are log-transformed,median-centered per array, and SD-normalized to 1 perarray. All tumor array data are described in Supplemen-tary Tables S2 to S4.8

Hierarchical clustering of MSC transformation geneexpression data was done using DNA-Chip Analyzer(dChip).9 Using a distance metric of 1-correlation and acentroid linkage method, significant clusters were identi-fied with a P cutoff of <0.001. Further significance ofclusters was done using the R package ‘‘pvclust,’’ whichcomputes an approximate unbiased P value for clustersusing a multiscale bootstrap resampling method (16).pvclust was done with both n = 1,000 and n = 10,000bootstrap with similar results (Supplementary Fig. S1).8

Significant clusters were identified with P < 0.05.

qRT-PCRqRT-PCR was done on an Eppendorf Mastercycler using

the following primer pairs: PPAP2C_F (5¶-ctgtatgtgcaggcac-gact-3¶) and PPAP2C_R (5¶-aaaggccaccaggaagaact-3¶), RNA-SEH2A_F (5¶-ccagaccatcctggagaaag-3¶) and RNASEH2A_R

3 Expasy (http://www.expasy.ch/enzyme/).

4 ArrayExpress (http://www.ebi.ac.uk/microarray-as/aer/).5 expO repository (https://expo.intgen.org/geo/home.do).6 National Center for Biotechnology Information Gene Expression Omnibus(ftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/SeriesMatrix/GSE2109).7 Oncomine (http://www.oncomine.org).8 Supplementary material for this article is available at Molecular CancerTherapeutics Online (http://mct.aacrjournals.org/).9 dchip (www.dchip.org).

RNASEH2A, PPAP2C, and ADARB1 as Cancer Targets250




(5¶-tgagtccctcctgattctcg-3¶), ADARB1_F (5¶-gtccgactcgaccat-gactt-3¶) and ADARB1_R (5¶-tgccagagacaggaggattt-3¶), andGAPDH_F (5¶-ggagtcaacggatttggtcgta-3¶) and GAPDH_R(5¶-ggcaacaatatccactttaccagagt-3¶). The reaction mixcontained 1� SYBR Green master mix (Applied Biosystems)and 0.5 mmol/L of each forward and reverse primers in avolume of 30 AL. PCR cycling consisted of 95jC for 10 minand then 40 cycles of 95jC for 30 s, 60jC for 60 s followed bya melt-curve analysis. Fold change in expression wascalculated by DDC t normalized to GAPDH for each sampleand normalizing each of the cell lines to the average of thecontrol cells: parental MSC (MSC 0), primary humanfibroblasts (HF 0), or transformed MSC (MSC 5).

Cell CulturePrimary MSC and each line containing a different set of

oncogenes (see Fig. 1) were grown in Mesencult medium

containing 10% human serum (Stem Cell Technologies) and1 ng/mL basic fibroblast growth factor (R&D Systems). Theprimary human dermal fibroblasts (HF 0), immortalizedhuman dermal fibroblasts (HF 1), transformed humandermal fibroblasts (HF 5), breast cancer cell line MCF7,leiomyosarcoma cell line SK-LMS1, and osteosarcoma celllines MG63 and U2OS were cultured in DMEM containing10% fetal bovine serum (Life Technologies/Invitrogen).

siRNA/shRNAKnockdown and In vitro AssaysKnockdown was done by transfection with a pool of four

siRNA oligonucleotides (100 nmol/L final concentration;Dharmacon) for each gene: RNASEH2A, ADARB1, andPPAP2C or ON-TARGETplus Nontargeting Control. Fourindividual siRNA oligonucleotides were also used forPPAP2C knockdown. Transfection was done using Oligo-fectamine reagent (Invitrogen) and the manufacturer’s

Figure 1. Gene expression micro-array data for 45 enzymes in theMSC cancer model. Three replicategene expression microarrays datafor each of the five transformingsteps (hits) is compared with theparental MSC (see ref. 14 for moredetails). The parental MSC wasimmortalized with hTERT (MSC1hit); then, pRb and p53 wereinactivated by addition of the humanpapillomavirus proteins E7 (MSC 2hits) and E6 (MSC 3 hits) followedby inactivation of PP2A and stabili-zation of c-Myc by the addition ofSV40 small T antigen (MSC 4 hits);finally, constitutively active H-Raswas expressed (MSC 5 hits).Enzymes that showed statisticallysignificant >2-fold increase in ex-pression in MSC 5 hits compared toparental MSC were selected andhierarchical clustering was done us-ing dChip as described in Materialsand Methods. Positive controlenzymes (c) described in Fig. 2 andSupplementary Fig. S2 and novelenzymes (b) described in Fig. 2 aremarked.

Molecular Cancer Therapeutics 251




protocol. Additional knockdown of RNASEH2A wasdone by transfection of shRNA vectors for RNASEH2A,nonsilencing control, and vector control from the OpenBiosystems whole-genome pGIPZ shRNA library usingFuGene reagent (Roche) and the manufacturer’s protocol.Transfection efficiency of shRNA vectors was estimated atf30% by monitoring green fluorescent protein from thevector. Following transfection, the cells were grown for48 h before harvesting for qRT-PCR or in vitro assays. Cellproliferation and viability was determined using theCellTiter 96 Aqueous One solution (Promega). Cells wereplated in triplicate at 1,000 cells per well of a 96-well plate.The CellTiter solution was added at both 0 and 48 h timepoints using 30 AL/well and further incubated for 2 h at37jC, and the absorbance was read at 490 nm to quantifythe formazan product. Anchorage-independent growth

was assessed by soft-agarose assays as described previous-ly (14). For cell cycle analysis, MCF7 cells were transfectedwith siRNA control pool or PPAP2C siRNA and grown for48 h followed by serum starvation for 24 h to synchronizecells in G1 phase. Cells were analyzed at 8 and 24 h post-serum release to assess S-phase progression. Cells wereharvested and fixed in 70% ethanol at 0jC, and the nuclearDNA was stained using a solution of propidium iodide(50 mg/mL), RNase A (1 mg/mL), and Triton X-100(0.02%) in PBS. Cell suspensions were analyzed on aFACSCalibur (Becton Dickinson) using CellQuest andModfit data analysis software.

Western BlotAntibodies used in this study include sc-126 (DO-1) for

p53 (Santa Cruz Biotechnology) and Ab-1 for actin(Oncogene).

Table 1. Gene list of up-regulated enzymes in transformed MSC cancer model

HGNCsymbol

HGNC name GeneRifs* PubMedcites*

Pathway/function PharmGKBdrugs/drug pathwaysc

RNASEH2A RNase H2, subunit A 0 11 RNA degradation NonePPAP2C Phosphatidic acid phosphatase type 2C 2 13 Glycerolipid

synthesisNone

ADARB1 Adenosine deaminase, RNA-specific, B1 10 58 RNA pre-mRNAediting

None

PRSS3 Protease, serine, 3 (mesotrypsin) 8 47 Trypsin inhibitordegradation

None

CTSH Cathepsin H 6 117 Lysosomaldegradation

None

MTAP Methylthioadenosine phosphorylase 12 108 Polyaminemetabolism

None

CPE Carboxypeptidase E 4 117 Neuropeptidecleavage

None

DHFR Dihydrofolate reductase 22 145 Purinebiosynthesis

Methotrexate,5-fluorouracil,antimetabolite,antineoplastic

agentsMMP3 Matrix metalloproteinase 3 95 257 Extracellular

matrixdegradation

Pravastatin

MMP1 Matrix metalloproteinase 1 148 311 Extracellularmatrixdegradation

Doxorubicin

PTGS2 Prostaglandin-endoperoxidesynthase 2 (cyclooxygenase-2)

549 616 Prostaglandinbiosynthesis

Aspirin, celecoxib,clomipramine,glucocorticoids,prostaglandins,rofecoxib, statin,

valdecoxib

NOTE: For full table of 44 enzymes, see Supplementary Table S1.*The number of GeneRifs or PubMed citations related to each enzyme (as of September 2007).cIdentification of known drugs or drug pathways that target each gene.bNumber of drug programs targeting each gene.xIdentification of patents identifying these genes specifically as cancer drug targets.kIdentified as a potential target for small molecules based on protein structure from Russ and Lampel (4).{Bioinformatic comparison with successful drug targets and ranking (percentile) from Mayburd et al. (5).





Other Online ResourcesWe screened for novelty of targets using Entrez Gene10

and PubMed11 and identification of known drug targetsfrom PharmGKB,12 Thomson Pharma,13 and TherapeuticTarget Database.14 Patent information was screened usingesp@cenet15 and Patent Lens.16

ResultsExpression of Enzymes in the Transformed MSC

ModelWe analyzed the expression of all known enzymes in the

human genome, with the aim of identifying novel enzymesthat are up-regulated in transformed MSC, and not in

primary MSC, and also in various cancers. Such enzymescould potentially be considered as new targets for cancerdrug development. We identified 56 probe sets, represent-ing 44 enzymes (one or two probe sets per gene), which aresignificantly up-regulated >2-fold (false discovery rate-corrected P < 0.05) in the transformed MSC (5 hits, MSC 5)compared with the primary MSC (0 hits, MSC 0; Fig. 1).Hierarchical clustering analysis of each of these probe setsidentifies four significant (P < 0.05) clusters that stratifywith the genetic alterations during stepwise transformation(Supplementary Fig. S1).8 The first cluster includes 4enzymes (UPP1, XDH, MMP3, and CPE) up-regulated onlyafter the fifth oncogenic hit, correlating with activation ofthe Ras pathway (constitutively active H-Ras) and the fullytransformed state. The second major cluster contains 22enzymes (from GCDH to RNMT) significantly increasedafter the inactivation of PP2A and stabilization of c-Myc(fourth hit), and a third cluster contains 17 enzymes (fromGPD2 to MTAP), which are significantly up-regulated afterthe inactivation of pRb. The fourth cluster contains 4enzymes (PTGS2, OAS1, NP, and PRSS3) and displays aninteresting profile being up-regulated after introduction of

Table 1. Gene list of up-regulated enzymes in transformed MSC cancer model (Cont’d)

Therapeutictarget database c

ThomsonPharmab

Patentsx Druggabletargetk

Successfulanticancer target{

Mayburdrank{

None None No No Yes 0.9845None None No No No 0.8114

None None No No No 0.3103

None None No Yes No 0.8400

None None No Yes No 0.7801

Research target None WO 2007/097648 A1 Yes No

Research target None No Yes No 0.4929

Lamotrigine;malarone;

pyrimethamine;pemetrexed;

proguanil; trimetrexate

45 WO 2007/132146 A1 Yes Yes 0.9782

Research target 9 US 2005/250789 A1, US 5473100 Yes Yes 0.3412

Research target 8 US 2005/250789 A1, US 5473100 Yes Yes 0.8068

Celecoxib;rofecoxib;valdecoxib

124 EP1239879 B1, EP1654262 B1 Yes Yes 0.9582

10 Entrez Gene (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene).11 PubMed (http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed).12 PharmGKB (http://www.pharmgkb.org/index.jsp).13 Thomson Pharma (http://www.thomson-pharma.com).14 Therapeutic Target Database (http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp).15 esp@cenet (http://gb.espacenet.com/).16 Patent Lens (http://www.patentlens.net/patentlens/structured.cgi).





hTERT (first hit) or p53 (third hit) suppression but thendown-regulated after the inactivation of PP2A (fourth hit)and again increasing on H-Ras activation. We have shownpreviously that these clusters are representative of genesknown to be transcriptionally regulated by each oncogenichit (Supplementary Fig. S68 in ref. 14). Furthermore, as anexample, we show that gene set enrichment analysisidentifies a significant overrepresentation of known Myctargets (P = 0.009262) in the genes significantly up-regulated or down-regulated after the fourth hit (17).

Identification of Known CancerTargetsThe majority of significantly up-regulated enzymes (32 of

44, 73%) in the transformed MSC either are already well-known cancer drug targets, act within known drug path-ways, or are current research targets previously identifiedas up-regulated in various malignancies (Table 1; Supple-mentary Table S1).8 This is confirmed statistically incomparison with the percentage of known target enzymesin those that do not change expression in this model (12%are known targets) or are significantly down-regulated(11% are known targets; P = 8.425e-08, m2 test). This can beconsidered as a positive control for the identification ofcandidate drug targets in this model and suggests thatthere may be few truly novel up-regulated enzymes in thismodel system left to be identified as cancer drug targets.We have identified four examples of well-characterizedcancer drug targets that are also identified as significantlyup-regulated in our transformed MSC cells: PTGS2 (cyclo-oxygenase-2), MMP1 and MMP3, and DHFR. Analysis ofthe expression of these candidates in the transformed MSCand in various cancer gene expression data sets revealsnovel insights into the regulation of these genes and otherenzymes with similar expression profiles (Fig. 1) and alsoidentifies novel cancers in which these genes are overex-pressed (Fig. 2A; Supplementary Fig. S2).8 As an example,we show that hTERT alone is capable of up-regulatingtranscription of PTGS2 in MSC with a 1.8-fold increase (P =0.039) compared with parental MSC. Furthermore, weobserve increased expression of PTGS2 in seminoma,chondromyxoid fibroma, and malignant peripheral nervesheath tumor, which have not been reported previously.

Structural Analysis of Druggable EnzymesA structural analysis of all known genes has been done

previously to identify the ‘‘druggable genome,’’ that is,proteins that contain structural elements amenable tosmall-molecule inhibition (3, 4). A comparison of the up-regulated enzymes in the MSC model and the druggableproteins identified 16 enzymes that are already targeted byspecific drugs (ODC1, MME, NP, ALDH1A3, ADCY3,RRM1, ASNS, PPAT, UMPS, SOAT1, XDH, DHFR, DPYD,MMP3, MMP1, and PTGS2). In addition, we have alsoidentified 4 enzymes that have been characterized asdruggable and are up-regulated in numerous cancers,although no drugs have yet been developed against thesetargets (MTAP, PRSS3, CPE, and CTSH; SupplementaryFig. S3).8 These 4 enzymes have been implicated previouslyin cancer and thus cannot be considered novel candidates(18–21).

Identification of Novel CancerTargetsNovelty of candidate enzymes was investigated using

several measures (Table 1). GeneRifs or ‘‘Gene Referenceinto Function’’ consist of phrases taken from publicationsdescribing a function of the gene or association withparticular diseases or cancers. The number of PubMedcitations for particular genes also serves as a measure ofhow well characterized it is, and a systematic search ofthe literature on PubMed was used to identify any knownassociations with cancers. Finally, PharmGKB, Therapeu-tic Target Database, Thomson Pharma, and Patent data-bases indicate whether the gene is already a target of aknown drug or acts within a known drug pathway.Specifically, if the gene is listed on any of the Web sitesas a drug target, then we have considered that as aknown target. Additionally, if the gene is within a verywell described pathway, such as the methotrexatepathway (Supplementary Fig. S4),8 then we have alsoconsidered this a known target. Taken together, thesemeasures have identified 3 enzymes (RNASEH2A,ADARB1, and PPAP2C), which are expressed in variousmalignancies, up-regulated in some cancers comparedwith normal tissues, and are candidate novel cancertargets (Table 1; Fig. 2).RNASEH2A is the catalytic subunit of the RNase H2

complex, which is the major source of RNase H activityin mammalian cells (22), and is up-regulated in MSCafter the inactivation of pRb and also in fully trans-formed MSC. It is also up-regulated in a variety ofdifferent cancer types including bladder, brain (glioblas-toma multiforme, oligodendroglioma, and oligoastrocy-toma), breast, head, and neck squamous cell carcinomasas well as leukemias (T- and B-cell acute lymphoblasticleukemia and acute myeloid leukemia), melanomas, andseminomas (Fig. 2B). It is expressed in all of thecarcinomas and sarcomas investigated with the highestexpression observed in breast carcinomas and dediffer-entiated chondrosarcomas, respectively. RNASEH2A wasalso one of the top 2% of genes (ranked 0.9845) identifiedby Mayburd et al. that showed significant correlationwith the profiles of all known successful cancer drugtargets (5).PPAP2C is a member of the phosphatidic acid phospha-

tase family of enzymes (also known as lipid phosphatephosphatases) that regulate the dephosphorylation oflipid phosphates (23). Compared with matched normaltissues, PPAP2C is significantly overexpressed in ovariancarcinoma similarly to other family members (ref. 24;Fig. 2C). However, our analysis has shown that it is alsooverexpressed in bladder, lung, and prostate cancerscompared with normal tissues. In the cancer gene expres-sion data, we detect expression of PPAP2C in allcarcinomas and sarcomas and it appears to be particularlyhighly expressed in breast carcinoma, dedifferentiatedchondrosarcoma, fibromatosis, synovial sarcoma, andneurofibroma. PPAP2C is also significantly overexpressedin high-grade breast tumors compared with low-gradetumors (Supplementary Fig. S5).8





Figure 2. Cancer gene expression microarray data for PTGS2 (A) and novel cancer drug targets (B-D). Gene expression microarray data obtained fromthe National Center for Biotechnology Information Gene Expression Omnibus for carcinomas and sarcoma gene expression data (15) are presented as boxplots of log2 expression values. The normal versus tumor gene expression data were obtained from Oncomine and is presented as log-transformed, median-centered per array, and SD-normalized to 1 per array. Only tumor types with significant overexpression compared with normal tissue (P < 0.0001) arepresented. Asterisk, novel cancers in which detectable levels of expression (log2 expression >5 in the carcinomas or sarcomas) or overexpression (innormal versus tumor comparisons) are detected for PTGS2. The full list of sarcoma abbreviations is listed in Supplementary Tables.





Adenosine deaminase is a key degradative enzyme ofpurine metabolism and is crucial for DNA replication andseveral purine nucleoside analogues have been developedto specifically target adenosine deaminase, includingpentostatin and cladribine (Thomson Pharma Web site10).There are, however, nine other members of the adenosinedeaminase family in the human genome, many of whichare RNA-specific including ADARB1, one of the candidatesidentified by our analysis. ADARB1 shows significant up-regulation in lymphomas and seminomas in comparisonwith normal tissues and is expressed consistently inmost carcinomas and sarcomas that we have investigated(Fig. 2D). Of note is the significantly increased expressionin chondromyxoid fibromas (CMF), a benign cartilaginous

bone tumor, compared with all other sarcomas. Theregulation of ADARB1 has not been described. We detectsignificant 5-fold increase in expression of ADARB1 in thegene expression microarray data (Fig. 1) and by qRT-PCR(data not shown) following the addition of small T antigen,which stabilizes the c-Myc oncogene, suggesting that Mycmay be involved in transcriptional regulation of thisenzyme.

Validation of Novel CancerTargetsWe have confirmed the increased expression of RNA-

SEH2A (4.8-fold; P = 0.012), ADARB1 (6.3-fold; P = 0.0002),and PAPP2C (7.7-fold; P = 0.0014) by qRT-PCR in thetransformed MSC (MSC 5 hits) compared with the parentalcells (Fig. 3A). We have also sought to confirm theoverexpression of these enzymes in transformed humanfibroblasts, which may be more representative of carcino-mas. These primary human fibroblasts were transformedwith the same five oncogenic hits as were used in the MSCand showed similar signs of transformation including theability to form colonies in soft agarose and tumors inathymic mice (14). In the transformed fibroblasts, signifi-cant overexpression was detected for both RNASEH2A(2.0-fold; P = 0.0058) and PPAP2C (6.2-fold; P = 0.00065)compared with the primary fibroblasts. However, ADARB1was not significantly different to the parental fibroblasts(Fig. 3B).Expression was also confirmed in each of the cancer cell

lines tested with similar expression levels to the trans-formed MSC and significantly lower expression in the twonormal cell lines (Fig. 3C).We have investigated the role of RNASEH2A, PPAP2C,

and ADARB1 in proliferation and transformation by siRNAknockdown in the transformed MSC, transformed humanfibroblasts, and the cancer cell lines MCF7 (breast cancer),SK-LMS1 (leiomyosarcoma), MG63 (osteosarcoma), andU2OS (osteosarcoma). We compared the cell viabilityresults in cancer cells with the immortalized normal celllines MSC 1 (MSC + hTERT) and HF 1 (human fibroblasts +hTERT; Fig. 4). Knockdown for each of the genes wasconfirmed by qRT-PCR analysis of mRNA levels in each ofthe cell lines tested (Supplementary Fig. S6).8 siRNAknockdown of RNASEH2A or ADARB1 significantlyaltered the cell viability of one osteosarcoma cell line,U2OS; however, neither gene altered the proliferation ofany of the other cell lines tested. Knockdown of PPAP2Csignificantly decreased the cell viability of all cancer celllines tested, except the leiomyosarcoma cell line SK-LMS1.In the normal immortalized MSC and fibroblasts, inhibitionof RNASEH2A and ADARB1 did not alter cell viability;however, PPAP2C knockdown altered the viability of theMSC but not the differentiated fibroblasts (Fig. 4A). Thesequence specificity of PPAP2C knockdown by the pooledsiRNA oligonucleotides was confirmed by knocking downexpression with the four oligonucleotides separately in thetransformed fibroblasts compared with the normal fibro-blasts (Fig. 4B).We have shown previously that transformed MSC form

colonies rapidly in soft agarose similarly to many cancer

Figure 3. Validation of overexpression of novel candidate enzymes.qRT-PCR was done for ADARB1, RNASEH2A, and PPAP2C in thetransformed MSC (MSC 5) compared with the parental MSC (MSC 0)(A) and in human dermal fibroblasts (HF 0) compared with transformedhuman dermal fibroblasts (HF 5) (B). Fold change in expression wascalculated in three replicates by DDC t normalized to GAPDH for eachsample and normalizing each of the cell lines to the average of the parentalcells. Student’s t test was done for statistical analysis. C, expression ofRNASEH2A, PPAP2C, and ADARB1 in cancer cell lines was calculated inthree replicates by DDC t normalized to GAPDH for each sample andnormalizing each of the cell lines to the average of the transformed MSCfor each of the genes separately.





cell lines (14). The transient siRNA or shRNA knockdownof RNASEH2A in transformed MSC significantly reducedthe number of colonies formed in soft agarose (Fig. 4Cand D). This finding was also observed in transformedfibroblasts HF 5, MCF7, SK-LMS1, and MG63. Theosteosarcoma cell line U2OS and the immortalized MSCand fibroblasts do not form colonies in soft agarose. Thisis the first evidence that RNASEH2A may play a role intransformation. Although ADARB1 knockdown sup-pressed soft-agarose colony formation in transformedMSC, we did not observe a similar result in any othercell line.In rat fibroblasts, the homologous gene, Ppap2c, caused a

premature entry into S phase when overexpressed anddelayed S-phase entry when knocked down (25). We haveconfirmed this in the human breast cancer cell line, MCF7,which expresses the highest level of PPAP2C and showed ahigh degree of inhibition of in vitro proliferation. We showthat PPAP2C siRNA knockdown in MCF7 delays S-phaseprogression and does not induce apoptosis (Fig. 5A).Furthermore, we show that PPAP2C is up-regulated inthe transformed MSC model after the addition of humanpapillomavirus E6 (which suppresses p53 function) and isalso up-regulated following shRNA knockdown of p53 oroverexpression of a dominant-negative form of p53 (Fig.5B-D). These data are consistent with a role for p53 in thetranscriptional regulation of PPAP2C.

DiscussionIn this study, we have sought to identify novel cancer drugtargets by investigating a transformed MSC model. Thismodel represents the oncogenic alterations that occur in thetransformation process and the transcriptional changesobserved are specific to each oncogenic hit in a homoge-nous cell type. We use this model in addition to studyingtumor tissue compared with normal tissue, which representthe molecular alterations that have occurred late in thetumor development (at the time of diagnosis and surgicalresection of the tumor) and in a heterogeneous cellpopulation.The approach that we have taken in this study provides

an excellent resource for investigating the regulation andexpression of established successful drug targets, as wehave shown that many of these enzymes are up-regulatedin this model. We have elaborated on four of these targets,PTGS2, DHFR, MMP1, and MMP3, which we observe areup-regulated in numerous cancer types and serve as abenchmark for the novel targets that we have alsoidentified. Despite the cardiovascular side effects of oneinhibitor (Vioxx), PTGS2 inhibitors (coxibs) are currently inclinical trials for various malignancies including colorectal,non-small cell lung, breast, prostate, and pancreatic cancers(26–28). PTGS2 was shown previously to be overexpressedin chordoma and chondrosarcoma; however, the use ofcoxibs for sarcomas has yet to be explored (29, 30). Our

Figure 4. Functional consequence of knockdown of RNASEH2A, PPAP2C, and ADARB1. A, transfection of pooled siRNA, individual siRNAoligonucleotides, or shRNA vectors was done to knockdown RNASEH2A, PPAP2C, and ADARB1 in the transformed MSC (MSC 5), transformed humanfibroblasts (HF 5), breast cancer cell line MCF7, leiomyosarcoma cell line SK-LMS1, osteosarcoma cell lines MG63 and U2OS, and immortalized MSC (MSC 1)and human fibroblasts (HF 1). Cell viability was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Data represent thepercentage growth compared with untreated cells calculated as a ratio of 48/0 h and an average of three replicate wells. B, transfection of individual siRNAoligonucleotides to PPAP2C was used to knockdown PPAP2C in transformed human fibroblasts (HF 5) and human fibroblasts (HF 1). C, growth of coloniesin soft agarose was done as described previously (14). The number of colonies was quantitated by counting colonies in three fields from three replicateexperiments. D, confirmation of the specificity of the knockdown with an additional shRNA vector. Student’s t test was done for statistical analysis. Bars,SE from triplicate experiments.





analysis identified several novel cancers in which PTGS2appears to be overexpressed including the difficult-to-treatsarcomas, chondromyxoid fibroma, and malignant periph-eral nerve sheath tumor. These data provide evidence thatcoxibs could be worth investigating in these tumor typesand suggest novel indications for these drugs. Methotrex-ate is one of the first clinically useful broad-rangeantimetabolic anticancer drugs that targets DHFR (31). Itis a drug that has been used for numerous cancer types andit is encouraging to see that the MSC model includes notonly up-regulation of DHFR but also many other enzymesinvolved in the de novo purine biosynthesis pathway(Supplementary Fig. S6).8 The MMP gene family includingMMP1 and MMP3 are also recognized as pharmaceuticaltargets (32). The challenge ahead for this class of drugs

appears to be in the development of specific active site-directed inhibitors against specific MMPs (32). Althoughour analysis has confirmed the overexpression of thesegenes in many cancers, we have also identified severalnovel tumors that express MMP1 (chondroblastoma) andMMP3 (chordoma and seminoma), suggesting that the fullrange of malignancies that the drugs against these targetscould be used for is yet to be explored (SupplementaryFig. S2).8 Similarly to these known targets, the novelenzymes that we have identified, RNASH2A, PPAP2C,and ADARB1, are overexpressed in numerous cancer typesand targeting these genes could have potentially broadtherapeutic options.The lysophosphatidic acid signaling cascade may be a

novel target for therapy in ovarian cancer (24). Indeed,

Figure 5. PPAP2C is involved in S-phase progression and is regulated by p53. A, transfection of siRNA oligonucleotides was done to knockdownPPAP2C in the breast cancer cell line MCF7. Cells were grown for 48 h subsequent to transfection, serum starved for 24 h to synchronize the cells in G1

phase (data not shown), and released for 24 h before fixing, staining with propidium iodide, and analysis by flow cytometry. Cell cycle phase percentageswere calculated by the Modfit software package. B, expression of PPAP2C was measured by qRT-PCR in the transformed MSC model showing up-regulation subsequent to the addition of human papillomavirus E6. C,Western blot analysis showing knockdown of p53 by E6 in the MSC model (MSC2-E6) compared with MSC2-E7 or by siRNA knockdown of p53 in MSC2-E7 compared with vector control or overexpression of a dominant-negative form ofp53 (p53-175H). D, expression of PPAP2C was measured by qRT-PCR in the cells with abrogated p53.





targeting the lipid signaling cascade is an emergingtherapeutic strategy for cancer, inflammation, and meta-bolic diseases (33). Furthermore, phosphatidic acid phos-phatase enzymes are involved in receptor-activated signaltransduction by the phospholipase D enzyme family, whichare key requirements for Ras-mediated transformation (34).Our data suggest that overexpression of PPAP2C, observedin numerous human cancers, may be a requirement forincreased cell proliferation. It is, therefore, not unexpectedthat its regulation is controlled in part by p53, which isknown to suppress many other cell cycle regulating genessuch as chk1, cdc20, cdc25A, and cdc25c (35). Our data alsoshow that the normal MSC are inhibited by the knockdownof PPAP2C, whereas the differentiated cells are not affecteddespite the similar expression levels. We suggest that this isdue to a common feature shared by both the cancer celllines and the normal stem cells, which could be the abilityfor self-renewal or an increased susceptibility to aberrantcell cycle signals. This particular enzyme family could beattractive targets as they are expressed on the plasmamembrane, with the active site facing the extracellularmatrix potentially allowing recognition by therapeuticantibodies.The RNase H2 complex is a heterotrimer composed of the

catalytic subunit RNASEH2A and the noncatalytic subunitsRNASEH2B and RNASEH2C, which together are able torecognize and cleave a single ribonucleotide embedded in aDNA-DNA complex (36). This complex also mediates theexcision of single ribonucleotides from DNA:RNAduplexes such as in the removal of the lagging-strandOkazaki fragment RNA primers during DNA replication(37). Thus, a plausible biological hypothesis for this gene asa candidate drug target is likely to involve its role in DNAreplication. It is now apparent that the role of RNA in thecell includes regulation of gene expression via RNAinterference pathways, heterochromatin formation, andtargeting DNA methylation and histone modifications (38,39). RNA regulating enzymes, therefore, could represent anovel class of enzymes that could be considered aspotential cancer drug targets. Inactivating mutations inthe three RNase H2 subunits have recently been implicatedin the autosomal recessive neurologic disorder Aicardi-Goutieres syndrome (36). If RNASEH2A is involved intransformation as suggested by our data, it would beinteresting to further investigate whether heterozygouscarriers of these Aicardi-Goutieres syndrome mutations areto a certain extent protected against cancer.ADARB1 is an RNA-specific adenosine deaminase,

which encodes an enzyme that is responsible for pre-mRNA editing of the glutamate receptor subunit B bysite-specific deamination of adenosines (40). Although wedetect no adverse effects of ADARB1 knockdown in thecancer cell lines tested, the role of RNA editing may play arole in brain tumor formation (41, 42). This has beendescribed as an epigenetic mechanism and may also helpor hinder miRNA functions by editing to interfere withpri-mRNA processing, altering target sites, or editing ofmiRNA sequences themselves (42).

In conclusion, the in silico approach investigating allknown enzymes in a transformed stem cell model is auseful approach that could equally be applied to investi-gate all G protein-coupled receptors, ion channel genes, orputative enzymes. In addition to identifying novel candi-date cancer targets, this genetic approach can also improvecurrent drug therapy in humans by identifying novelcancers in which known targets are up-regulated andprovide further insight into the oncogenic regulation ofthese known targets. We have identified three enzymes,RNASEH2A, PPAP2C, and ADARB1, which, to ourknowledge, are novel putative candidate cancer drugtargets. We have confirmed the overexpression in cancercell lines and have shown that inhibition of two of thesecandidates, RNASEH2A and PPAP2C, leads to a reductionin the in vitro proliferation or transformation of cancer cells.This suggests that our analytical screen, combining bothour in vitro model and public gene expression data, mayhave enriched for genes that alter the proliferation of cancercells. However, the extent to which this analysis hasenriched for true drug targets can only be determinedwith additional screening of more genes compared with avariety of genes that were not hits in this screen. Both ofthese identified genes could be classified as ‘‘non-oncogeneaddiction’’ genes that are required for continued cancer cellproliferation but not essential for normal differentiated cellproliferation. This category of genes is the most desirablefor targeting for cancer therapy (10). However, validationof these genes as true cancer targets can only be claimedonce a therapeutic agent is developed and shown to beclinically effective by acting against the target to which itwas designed (43).

Disclosure of Potential Conflicts of InterestN. Carey: CellCentric grant support, CellCentric employee. J. Flanagan:CellCentric-sponsored post-doctoral fellow. No other potential conflictsof interest were disclosed.

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2009;8:249-260. Mol Cancer Ther James M. Flanagan, Juan M. Funes, Stephen Henderson, et al. drug targetsRNASEH2A, PPAP2C, and ADARB1 as putative anticancer Genomics screen in transformed stem cells reveals

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