Cancer Investigation, 27:541–548, 2009ISSN: 0735-7907 print / 1532-4192 onlineCopyright c! Informa Healthcare USA, Inc.DOI: 10.1080/07357900802563044

ORIGINAL ARTICLEClinical Translational Therapeutics

DNA Copy Number Variation and Gene ExpressionAnalyses Reveal the Implication of Specific

Oncogenes and Genes in GBMJavier Margareto,1 Olatz Leis,2,3 Eider Larrarte,1 Inigo C. Pomposo,2,3 Jesus Marıa Garibi,2,3

and Jose Vicente Lafuente3

LEIA Foundation, Genomics Unit, LEIA-InnoSalud. Leonardo de Vinci, 11, Alava Technologic Park, Postal Code 01510, Minano (Alava), Spain,1

Neurosurgery Service, Cruces Hospital, Plaza de Cruces S/N, E48903 Cruces (Barakaldo), Spain,2 and LaNCE, Laboratory of Clinical andExperimental Neuroscience, Department of Neurosciences, Faculty of Medicine, Basque Country University, Barrio Sarriena S/N Postal Code

E48940, Leioa (Bizkaia), Spain.3

ABSTRACTTo understand the pathogenesis of glioblastoma multiforme (GBM) we used high-resolution

comparative genomic hybridization arrays and gene expression microarrays to identify DNAcopy number alterations and gene expression changes in comparable sets of GBM samples.Gains were detected at chromosomes 1, 2, 7, 9, 12, 19, and 20 and losses at 6, 9, and 10.Gene expression analyses identified specific genes overexpressed in GBM mapping at am-plified chromosomal regions. Among these genes we found genes involved in angiogenesis,extracellular matrix remodeling and several oncogenes. DNA copy number analysis along withgene expression profiles provides a powerful strategy to understand tumor progression andidentification of genes involved in GBM pathogenesis.

INTRODUCTIONGliomas are the most common primary brain tumors in

adults, constituting about 40% of the primary intracranial neo-plasm and 78% of all malignant central nervous system tumors(1). The World Health Organization (WHO) (2) has dividedastrocytic gliomas into four malignancy degrees in which sur-vival decreases with increasing grade. Pilocytic astrocytomas(WHO grade I) are generally slow-growing and noninfiltrativepediatric tumors. Low-grade astrocytomas (LGA), WHO gradeII astrocytoma, show little anaplasia and mitotic activity. LGAtumors grow slowly but have an inherent tendency for malig-nant progression to anaplastic astrocytoma (AA), WHO grade

Keywords: CGH array, Glioblastoma, Copy number gains, GeneexpressionCorrespondence to:Javier Margareto SanchezLEIA Foundation, Pharmaceutical Development UnitLeonardo de Vinci 11, Alava Technologic ParkPostal Code 01510Minano (Alava), Spainemail: javierm@leia.es

III. Glioblastoma multiforme (GBM), also grade IV astrocy-toma, is the most malignant grade of astrocytomas. GBMs arehighly anaplastic, usually entail an extremely poor prognosisand depict about half of all astrocytic gliomas. Median over-all survival (OS) for patients with LGA is typically more than5 years. Patients with AA survive an average of 3 to 5 years,whereas survival drops to less than a year for GBM (3).

Diagnosis of brain tumors is often controversial. Histolog-ically, GBMs are characterized by high mitotic activity, nu-clear atypia, microvascular proliferation, and necrosis. Never-theless, strong histological variability exists within the sametumor grade.

In this sense, tumor gene expression profiling using DNA mi-croarrays represent a helpful approach to identify specific genesor gene sets associated with tumor grade and progression thatcould improve clinical management of gliomas (4). In additionto gene expression, and similarly to other solid tumors, chro-mosome abnormalities and gene copy number variations havebeen shown to play an important role in the malignancy of GBM(5, 6). Some of these genomic alterations in GBM have beendescribed to occur at relatively high frequency throughout thegenome. These modifications include gains in chromosomes 1,4, 7, 12, 19, and 20 and losses in chromosomes 6, 9, 10, 13,

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and 14 (7, 8). Amplified and lost chromosomal regions includewell-known oncogenes, tumor suppressor genes, or cell cycleregulators.

The recently developed technique, comparative genomichybridization-based array (CGH array), permits high-resolutiondetection of DNA copy number imbalances as well as to mapsuch copy number alterations throughout the genome (9). Inthis sense, the utilisation of CGH arrays to search for DNAcopy number variations (CNVs) throughout the genome alongwith high-throughput gene expression profiling techniques mayhelp understand the progression and malignancy of GBM tu-mors as well as to identify target genes that could be involvedin the pathogenesis of the tumor.

Present work focuses on the characterization of DNA imbal-ances in a set of GBM samples. We have used high-resolutionCGH arrays to survey a set of glioblastoma tumor samples in or-der to detect DNA CNVs. We detected frequent gains at severalchromosomal regions 1q, 2q, 7p, 7q, 12p, 12q, 19q, and 20q.Similarly, losses at regions 6q, 9p, and 10q are also reported.CGH arrays also allowed the identification of genes mapped atthe amplified/lost chromosomal regions.

We also used Affymetrix arrays to profile gene expressionchanges in a group of comparable GBM biopsies. Remarkably,even though different sets of samples were used to search forDNA imbalances and gene expression changes, interesting genematches were found.

Combined usage and analyses of both types of technolo-gies and data sets may represent a helpful approach to identifyand understand the underlying molecular events responsible forchanges in gene expression patterns reported in GBM with re-spect to tumor-free samples. Such changes on gene expressionmay bring about the alteration of crucial cellular processes andmetabolic pathways, which in the long term lead the cell to amalignant and pathological state.

MATERIALS AND METHODS

Samples and tissue preparationFive previously GBM-diagnosed brain samples, belonging

to the Neurooncological Brain Donor’s program, were usedfor genomic CNV profiling study. The donors’ mean age was67 years. A representative tumor grade area was selected andexcised, avoiding necrotic, hemorrhagic, and edematous areas(Table 1).

Table 1. Clinical and CNV Data of GBM Donors

Case Sex Age Surv 1q21 1q32 2q32 7p/q 9q22 12p13 12q13 19q13 20q13 10q

68/06 M 69 15 A A A A A A A A A L69/06 M 68 266 A A A A A A A L70/06 M 57 242 A A A A L11/07 H 71 485 A A A39/07 H 74 464 A

Surv, survival time (days); A, amplification; L, loss.

Similarly, for the gene expression analysis a set of 6 biop-sies from GBM-diagnosed patients were used. Patients’ meanage was 59 years. In order to screen and evaluate gene ex-pression differences between controls and GBM samples, threecontrol tumor-free samples were taken from lobotomies of pa-tients diagnosed with epilepsy. Excised samples were split intotwo homogenous pieces. One was used for histopathologicalstudies and classification and the remaining part was immedi-ately frozen and stored at "80#C. All patients gave informedconsent before collection of tissue, according to guidelines ap-proved by the Institutional Review Board. Once biopsies werehistopathologically diagnosed and determined as comparable toGBM from donors, in terms of tumor grade and histologicalcharacteristics, the gene expression profile was studied.

It is important to remark that before isolation of DNA andRNA, the morphological features of each sample set were eval-uated according to the WHO classifications of brain tumors bytwo neuropathologists.

Nucleic acids isolationHigh-molecular-weight genomic DNA from tumors was iso-

lated following QiaAmp DNA mini Kit (Qiagen, Alameda, CA,USA) protocols. DNA concentration and integrity was deter-mined spectrophotometrically.

Total RNA was isolated using the Trizol RNA isolationmethod (Invitrogen, Paisley, UK), and purified with the QiagenRNeasy Mini Kit spin columns (Qiagen). RNA concentrationwas determined spectrophotometrically and RNA integrity wasconfirmed by electrophoresis of samples on a 1% agarose gel.

Array-CGH analysisGlioblastoma samples were analyzed using 4 $ 44 k CGH

Microarrays (Agilent Technologies, Santa Clara, CA, USA).These microarrays contain 42,494 probes. Probe coverage spansboth coding and noncoding regions, with emphasis on well-characterized genes, particularly cancer-relevant genes.

Hybridizations were carried out following Agilent Oligonu-cleotide Array-based GCH protocol. For each hybridization,1.5 µg of genomic DNA from tumor samples and referenceDNA (Invitrogen) were digested with AluI and RsaI (Promega)for 2 hr at 37#C.

Digested tumor and reference samples were purified, dena-tured at 95#C for 3 min and labeled with Cy5-dUTP and Cy3-dUTP respectively using the Agilent Genomic DNA labeling

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Kit at 37#C for 2 hr. Labeled tumor samples and sex matchedcontrol-reference DNA were co-hybridized in a rotating hy-bridization oven at 65#C for 24 hr. Two hybridizations with fluorreversal (Dye swap) were performed for each tumor sample. Ar-rays were washed in 0.5$ SSC/Lauroylsarcosin (Wash Buffer 1)at room temperature for 5 min and 0.1$ SSX/Lauroylsarcosin(Wash Buffer 2) at 37#C for 1 min.

After washes slides were scanned by using an Axon GenePix4100A confocal scanner. Scanned array images were analyzedusing GenePix Pro 6.1 software (Molecular Devices, Inc.,Sunnyvale, CA, USA). Background substracted Cy5/Cy3 flu-orescence ratios were normalized using the Lowess method.

Gene expression analysisGene expression analysis on Human Genome-U133A Arrays

(Affymetrix, Santa Clara, CA, USA), consisting of 22,000 oligo-probes (representing 14,500 well-characterized human genes),were performed as previously described (4).

Arrays were scanned using a confocal scanner (AgilentGeneArray Scanner, Affymetrix). Scanned array images wereanalyzed using the Affymetrix Gene Chip Operating Software(GCOS 1.2).

Microarray data analysisArray-based CGH data analysis was performed with Nexus

3.0 software (Biodiscovery, Inc., El Segundo, CA, USA). Thissoftware uses the Rank Segmentation algorithm together withthe statistical STAC (Significance Testing for Aberrant Copynumber) method (10) to identifiy nonrandom genomic ampli-fications and losses across multiple experiments. According tothese algorithms, two regions are considered different (segmen-tation) when p values are lower than 1.0E–5. Genomic regionsof gains were defined as averaged log2 CGH fluorescence ratio%0.2 and losses as averaged log2 CGH ratio & "0.3. Log2 ra-tios %0.5 were considered as high copy number gains and log2

ratios & "0.7 as big copy number losses.Affymetrix gene expression microarray data was analyzed

using the GeneSpring GX software v 7.3.1 (Agilent Technolo-gies).

To normalize data, values below 0.01 were first transformedto 0.01. Each measurement was then divided by the 50.0th per-

centile of all measurements in that sample. After that, each genewas divided by the median of its measurements in all samples.Once data were normalized, consecutive filtering steps wereperformed in order to remove noise derived from absent genes,background, and nonspecific hybridizations.

To test for significant differences between control andglioblastoma samples, mean expression levels between twogroups of samples were statistically compared using the Welcht-test analysis (as two groups were compared). The result is aset of genes for which the specified comparison shows statisti-cally significant differences in the mean normalized expressionlevels. Comparisons were performed for each gene, and thosewith the most significant differential expression (p-value cutoff<0.05) were returned.

MIAME ARRAY EXPRESSAll microarray data has been curated and accepted in the

ArrayExpress database. This is a public repository for microar-ray data, which is aimed at storing Minimal Informaton AboutMicroarray Experiments (MIAME)-compliant data in accor-dance with Microarray and Gene Expression Data (MGED)recommendations. The ArrayExpress Data Warehouse storesgene-indexed expression profiles from a curated subset of ex-periments in the repository. The data may be accessed at Ar-rayExpress Web page (http://www.ebi.ac.uk/arrayexpress), withexperiment accession numbers E-MEXP-567 for the gene ex-pression data set and E-MEXP-1562 for the array CGH data set.

RESULTS

CGH and gene expression array analysis inprimary GBM samples

We analyzed DNA copy number variations in a set of five pri-mary glioblastoma samples using Agilent 4 $ 44k CGH arrays.All hybridizations were done in duplicate (dye-swap); thus a setof 10 arrays were analyzed for DNA copy number alterations.Figure 1 shows the results obtained after analysis in terms offrequencies of copy number gains and losses across the genome.A detailed search into the chromosomal regions being amplifiedrevealed a number of 1,261 genes mapped at these locations.

Figure 1. Frequencies of copy number gains and losses across the genome (chromosomes 1 to 22). Solid black horizontal lines indicate gainsor losses in 40% of the samples analyzed. Copy number gains (green) and losses (red) are mapped relative to their position according to theNCBI database.

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Regarding gene expression analysis, Affymetrix Genome-U133A Arrays were used to survey gene expression in 6 patientsdiagnosed with brain tumor malignancies (GBM, WHO gradeIV) in relation to 3 control tumor-free samples. Welch t-testparametric test (p-value cutoff <0.05) identified 2,239 genesshowing statistically significant differences between control andGBM groups.

DNA copy number variations in GBMDNA copy number variations in GBM samples together with

clinical information of donors is summarized in Table 1.Our results showed High copy number gains of almost the

whole chromosome 7 in 4 of 5 samples analyzed. Amplified re-gions in chromosome 7 include several oncogenes, interleukins,and cell cycle– and cell proliferation–related genes (Table 2).Also, regions of approximately 2.4 Mb at location 1q21, 4.8 Mbat location 2q32, and 1.4 Mb at location 20q13 showed DNAgains in at least 3 of 5 samples analyzed. Additionally, regionsof 0.4 Mb at location 1q32, 0.5 Mb at location 9q22, 0.8 Mbat 12p13, 0.4 Mb at location 12q13, and 2.2 Mb at 19q13 werealso found amplified in 2 of 5 samples analyzed.

Amplified region at 1q21 includes genes involved in cell–cell interaction and inflammation (integrin alpha 10, ITGA10),xenobiotic metabolism–related enzymes (flavin containingmonooxygenase 5, FMO5) and cell proliferation–related genes(CD160). Amplified region at 1q32 comprises genes like KISS1(Kisspeptin) and the Protein Phosphatase 1, Regulatory (in-hibitor) subunit 15 (PPP1R15) (Table 2).

With respect to chromosome 2, genes mapping at amplifiedregion 2q32 include ITGA5 (integrin alpha 5) andCOL5A2 (col-lagen, type V, alpha 2).

Chromosome 20 showed and amplified the region at lo-cation 20q13. Among the genes contained in this region wefind ADPN (patatin-like phospholipase domain containing 3),BCAS4 (breast carcinoma amplified sequence 4), and PTPN1(protein tyrosine phosphatase, nonreceptor type 1).

Amplified regions in chromosomes 9 and 12 include geneslike GADD45 (growth arrest and DNA-damage-inducibleV) andCKS2 (CDC28 protein kinase regulatory subunit 2) in chromo-some 9, and TNFRSF1A (tumor necrosis factor receptor su-perfamily, member 1A), the tumor suppressor gene ING4 (in-hibitor of growth family, member 4) in chromosome 12 (Table2). Amplified region at location 19q13 includes numerous zincfinger proteins LGALS13 (lectin, galactoside-binding, soluble,13), LGALS14 (lectin, galactoside-binding, soluble, 14), CEA-CAM 8 (carcinoembryonic antigen gene 8), and oncogenes suchas AKT2 (v-akt murine thymoma viral oncogene homolog 2) andRAB4B (RAB4B, member RAS oncogene family) (Table 2).

In regard to DNA losses, a big DNA loss of 88 Mb wasdetected at region 10q11 in 3 of 5 samples analyzed (Table2), which includes relevant genes, like ADD3 (Adducin 3) andPTEN (phosphatase and tensine homologous gene), althoughlog2 ratios for this gene ("0.23) did not reach the minimumcriteria stablished to be considered a lost genomic region (log2

ratio &–0.3). Losses at chromosome 6 from q14 to q25 and

Table 2. List of genes showing copy number gains and losses

Gene Average Location Name

CD160 0.20 1q21.2 CD160 moleculeITGA10 0.23 1q21 Integrin, alpha 10KISS1 0.90 1q32 KiSS-1 metastasis-suppressorPPP1R15B 1.02 1q32.1 Protein phosphatase 1,

regulatory (inhibitor) subunit15B

EGFR 1.64 7p12 Epidermal growth factorreceptor

MET 1.17 7q31 Met protooncogeneWNT2 0.44 7q31 Wingless-type MMTV

integration site familymember 2

WNT16 0.50 7q31 Wingless-type MMTVintegration site family,member 16

BRAF 1.25 7q34 V-raf murine sarcoma viraloncogene homolog B1

RALA 0.34 7p15 v-ral simian leukemia viraloncogene homolog A

CKS2 1.50 9q22 CDC28 protein kinaseregulatory subunit 2

GADD45G 1.54 9q22.1 Growth arrest andDNA-damage-inducible, !

TNFRSF1A 0.49 12p13.2 Tumor necrosis factor receptorsuperfamily, member 1A

ING4 1.07 12p13.31 Inhibitor of growth family,member 4

LGALS13 1.48 19q13.1 Lectin, galactoside-binding,soluble, 13 (galectin 13)

LGALS14 0.64 19q13.2 Lectin, galactoside-binding,soluble, 14

AKT2 0.49 19q13.1–q13.2 V-akt murine thymoma viraloncogene homolog 2

RAB4B 1.30 19q13.2 Member RAS oncogene familyATF5 0.58 19q13.3 Activating transcription factor

5PTPN1 1.10 20q13.1–q13.2 Protein tyrosine phosphatase,

nonreceptor type 1BCAS4 0.68 20q13.13 Breast carcinoma amplified

sequence 4ADNP 0.43 20q13.13 Activity-dependent

neuroprotector homeoboxADD3 "0.36 10q24.2–q24.3 Adducin 3

Average log2CGH ratio data (average) are provided. Log2 CGH ratio%0.2 identify DNA copy number gains and log2 ratio %0.5 wereconsidered as high copy number gains. Log2 CGH ratio &"0.3 identifyDNA copy number losses.

chromosome 9 from p24 to p21 were also detected in 2 of 5samples analyzed.

Correlation between DNA CNVs and geneexpression changes data sets in comparable

GBM samplesAs changes in DNA copy number may have a direct impact

on gene expression, we tried to find any association between datasets derived from DNA CNV experiments in GBM samples andthe results obtained after gene expression analyses.

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Statistical analyses of gene expression data identified 2,239(p < 0.05) differentially expressed genes when comparing aset of samples derived from GBM biopsies with respect to aset of control tumor-free samples. Of this 2,239-gene list, 1,377genes were upregulated in GBM samples as compared to controlsamples whereas 862 were downregulated in GBMs.

Although coming from different sources, comparative analy-ses of CNV data with gene expression data revealed that 106 of1,377 (about 8%) of the genes showing significant high expres-sion levels in GBM biopsies mapped at corresponding ampli-fied DNA regions detected by the CGH arrays in GBM samplesfrom donors. Among these genes we found TXNIP (thioredox-ine interacting protein) at chromosomal location 1q21, COL5A2(collagen, type V, alpha 2) and ITGA5 (integrin alpha 5) at 2q32,RALA (v-ral leukemia viral oncogene homologue A), IGFBP3(insulin growth factor binding protein 3) and COL1A2 (colla-gen, type I, alpha 2) at chromosome 7, CDK4 (cyclin-dependentkinase 4) at 12q and AXL (AXL receptor tyrosine kinase), APOE(apolipoprotein E) and APOCII (apolipoprotein CII) at 19q13(Table 3).

DISCUSSIONWe have used high-resolution CGH microarrays to profile

chromosomal aberrations, specifically DNA copy number vari-ation, in a set of GBM samples. It is well known that, as ithappens in most solid tumors, GBM progression to malignancyis the result of a multistep process in which genomic DNA al-teration plays a very important role, affecting downstream keycellular processes such as gene expression.

The application of DNA microarray technology to conven-tional CGH technologies makes it possible to achieve a reliablehigh-resolution mapping of unbalanced genetic alterations, in-cluding DNA gains and losses (5). Moreover, the identificationof the impact of DNA copy number variation on gene expressionsheds light on the pathology of these tumors as well as it opensthe way to the detection of new diagnostic and/or prognosticmarkers.

In this sense, we have used Agilent 4 $ 44k CGH arrays toprofile DNA copy number variations in a set of GBM samplescoming from donors. These high-resolution arrays permit the

Table 3. List of genes showing amplification and overexpression in glioblastoma samples

Name Genbank Amp Avg Exp Avg Location Description

TXNIP NM 006472 0.56 1.5 1q21.2 Thioredoxin interacting proteinCOL5A2 AL575735 0.42 4.1 2q14–q32 Collagen, type V, alpha 2ITGAV AI093579 0.41 0.90 2q31–q32 Integrin, alpha 5RALA NM 005402 0.34 1.25 7p15 v-ral leukemia viral oncogene homolog ACOL1A2 NM 000089 0.69 4.17 7q22.1 Collagen, type I, alpha 2CDK4 NM 000075 3.6 1.56 12q14 Cyclin-dependent kinase 4AXL NM 021913 0.56 1.16 19q13.1 AXL receptor tyrosine kinaseAPOE NM 000041 0.20 1.55 19q13.2 Apolipoprotein EAPOC2 NM 000483 0.51 2.07 19q13.2 Apolipoprotein C-II

Average log2CGH ratio (Amp Avg) and average gene expression log2 ratio (Exp Avg) data are provided.

identification of gained and/or lost chromosomal regions butalso to identify and map the genes contained within those re-gions. Additionally we have used Affymetrix arrays to searchfor gene expression changes in a set of GBM biopsies excisedfrom patients undergoing surgery. It is important to note thatspecial care was taken in the biopsy selection process to in-clude those with the highest similarity to samples obtained fromdonors, so that GBM biopsies were comparable and similar toGBM samples used for CNV profiling in terms of tumor gradeand histopathological characteristics.

Intererestingly, we have detected frequent high copy numbergains at chromosomal regions 2q32. Moreover, we report spe-cific genes relevant for tumor progression, mapping at this regionas being also overexpressed in comparable GBM biopsies.

Chromosome 2 amplifications have not been previously doc-umented, at least in relation to human glioblastoma. This is a4.8-Mb region that includes no more than 17 genes. Amongthese it is important to note the presence of ITGA5. Integrins,and specifically ITGA5, have been demonstrated to play a directrole in cancer progression, tumor cell survival, and tumor an-giogenesis (11, 12). Also, collagen-related genes, COL5A2 andCOL3A1 (collagen, type III, alpha 1), which have been involvedin extracellular matrix (ECM) remodeling, were identified. Re-markably, we found that ITGA5 and COL5A2 genes were alsooverexpressed in glioblastoma biopsies. Precisely, one of thecharacteristics of this type of tumor is enhanced angiogene-sis and ECM remodeling (13), probably through an increasedexpression of genes involved in such processes. Our results sug-gest that the overexpression of crucial genes governing theseprocesses might be related to some genomic DNA instability.

Additionally, some of the results reported here agree withprevious observations regarding amplification at regions 1q, 7p,7q, 9q, 12 (p and q), 19q, and 20q. Moreover, further comparisonof CGH data with gene expression data set reveals some inter-esting, yet unpublished, data concerning specific gene mappingat those locations (see summary in Table 3).

DNA gains at region 1q have been documented through thebibliography, specifically at regions 1q32 (6) and 1q21 (5). Ourdata also agree with these results, which showed gains of a smallregion, 0.4 Mb, at 1q32 including PPP1R15B and KISS-1, awell-known metastasis suppressor gene in certain tumor types

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(14, 15). Furthermore, we also detected another amplified regionof approximately 2.4 Mb at 1q21. This region includes genesinvolved in cell proliferation, like CD160, and cell matrix ad-hesion,ITGA10, xenobiotic metabolizing enzymes, FMO5, andTXNIP gene. Interestingly, TXNIP gene was also found overex-pressed in GBM biopsies, suggesting a potential consequenceof DNA amplification on the regulation of the expression of thisgene in the tumoral cell. The impact of TXNIP overexpressionis not clear. Some authors consider that TXNIP might be actingas a metastasis suppressor gene, (16) thus inhibiting metasta-sis in certain tumor types (17). These reports together with ourresults regarding TXNIP locus amplification and gene overex-pression, as well as those of KISS1 locus amplification, couldbe related to the scarce metastatic activity that characterizesglioblastoma tumors. Moreover, other works have related theexpression of TXNIP gene with the hypoxic conditions of tumormicroenvironments and claim that this gene may be consideredas a hypoxic perinecrotic marker (18). Anyhow, contribution ofTXNIP to GBM progression must be investigated.

In agreement with previous works, DNA amplification at 7pand 7q, one of the most characteristic DNA imbalances in astro-cytic tumors, is also reported here (6, 19–21). Amplification ofchromosome 7 was observed in most of the samples analyzed.Chromosome 7 includes important oncogenes known to be in-volved in the progression and malignancy of glioblastoma suchas EGFR (endothelial growth factor receptor), which is the prin-cipal oncogene amplified in GBM (13). Other oncogenes locatedwithin this amplified region include MET (hepatocyte growthfactor receptor), RALA (v-ral simian leukemia viral oncogenehomolog A), and BRAF (v-raf murine sarcoma viral oncogenehomolog B1). Also genes involved in the Wnt pathway are foundin this region such as WNT16 (wingless-type MMTV integra-tion site family, member 16) and WNT2 (wingless-type MMTVintegration site family, member 2). Implication of Wnt pathwayin the development of brain tumors has been documented inprevious works (22, 23), which, together with our results aboutamplification of these loci, could suggest some implication ofWnt genes in the malignancy of glioblastoma. Additionally, wedetected that some of the genes mapping at amplified regionsin chromosome 7 showed a significant overexpression in GBMbiopsies also as compared to control tumor-free samples. Specif-ically, we found overexpression of genes such as the COL1A2(collagen, type I, alpha 2), which promotes ECM remodelingduring angiogenesis (24); oncogene RALA, a member of theRal GTPases family which is known to promote tumorigenesisand tumor progression; and IGFBP3 (insulin-like growth fac-tor binding protein 3), which encodes a positive regulator ofangiogenesis and cell proliferation and whose overexpressionhas been reported to predict an unfavorable prognosis in breastcancer (25). Present data seem to indicate that overexpressionof relevant genes for tumor progression in tumor cells could bea consequence of the DNA imbalances occurring in these cells.

Amplification of region 20q13 in human gliomas and specif-ically gene PTPN1 (protein tyrosine phosphatase, non-receptortype 1) has been previously described (26). Genes mapping atthis location include BCAS4, which is known to be amplified in

breast cancer (27), and ADNP, also amplified in other humancancer types (28).

With respect to chromosome 12, two regions at 12p13 andat 12q13 were amplified. Genes in these regions include TN-FRSF1A, a member of the TNF family that can activate theNFKB (nuclear factor of kappa light polypeptide gene enhancerin B cells) pathway. This cellular pathway is known to mediateinflammation and brain edema, a characteristic event of glioblas-toma progression and one of the most deleterious epiphenomenain GBM. Also, ING4 tumor suppressor gene is found at this re-gion. Interestingly, it is important to mention the amplificationof CDK4 gene locus in GBM samples from donors, as CDK4was also found overexpressed in GBM biopsies with respect tocontrol samples. This gene enhances cell passage through theG1/S checkpoint, leading to cell proliferation (29). Accordingto these results, it is tempting to postulate that upregulation ofCDK4 might be playing a key role in the progression of GBMand that this upregulation could be a consequence of gene am-plification in tumor samples (30, 31).

Regarding chromosome 19, an amplified region of 2.2 Mbwas found at 19q13. Amplified region at chromosome 19 in-clude several oncogenes, such as AKT2 and RAB4B (memberRAS oncogene family). 19q13 also includes cell proliferation–related genes SERTAD1 and 3 (SERTA domain containing 1 and3), which have been found amplified in several cancer types andare thought to contribute to oncogenesis (32). Additionally, sev-eral galectins were also detected at this region. These proteinsmediate cell–cell and cell–matrix interactions and have been de-scribed as potential mediators in tumor progression and metas-tasis (33, 34). What is also remarkable is the presence of the hy-poxia inducible gene HIF3A (hypoxia inducible factor 3, alphasubunit) in this region. Hypoxia-inducible factors are known toprotect cancer cells under stress conditions by inducing the tran-scription of several factors that promote tumor growth (35). Am-plification of this locus might result in a protection mechanism ofglioblatoma cells, as this factor has been recently published as acomplementary factor to the HIF1A (hypoxia inducible factor 1,alpha subunit) gene (36), a well-known transcription factor thatcontrols gene expression in key functional processes of cancercells. Furthermore, some genes mapped at the amplified region19q13 were also found overexpressed in GBM biopsies. Amongthese genes we found apolipoprotein genes APOE and APOCII.Apolipoproteins, specifically APOE, have been related to brainmalignancies such as Alzheimer disease (37). Also these pro-teins have been postulated as anti-inflammatory factors (38).Nevertheless, it has been reported that APOE may be involvedin the delivery of lipids to tumor cells and the recycling of lipidsby macrophages in tumor necrotic areas (39). Thus, overex-pression of these genes, probably because of amplification atcorresponding chromosomal location, might be affecting lipidtrafficking in tumor cells. Moreover, it is noteworthy that theamplified region at 19q13 also includes the oncogenic receptortyrosine kinase, AXL gene. This gene has been proposed to playa role in the mechanism of resistance to chemotherapy (40) andpostulated as a cancer cell survival factor (41) in several tumortypes. Furthermore, Hutterer et al. have recently reported that

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overexpression of AXL is a poor prognostic marker in patientswith GBM (42). Present results show, for first time, the corre-spondence between genomic amplification and gene expressiondata regarding AXL, as this gene was also found overexpressedin GBM biopsies. AXL has been proposed to exert its effectsthrough the AKT pathway (41). In this sense AKT2, a memberof the Akt pathway, was also amplified in GBM samples fromdonors, although no overexpression was detected in GBM biop-sies as compared to controls. These data support further studiesto be carried out in order to elucidate the role of AXL in GBMprogression.

On the other hand, besides DNA amplification, importantDNA losses were detected in GBM samples at 6q, 9p, and es-pecially at region 10q11. In this sense, loss of heterozygosity ofchromosome 10 has been reported as one of the most commonDNA aberration of high-grade gliomas (43). Among the genesmapping at this region, we found ADD3, which could play acrucial role in the progression and malignancy of GBM. Down-regulation of this gene has been described in astrocytomas asassociated with increased migratory activity of tumor cells (6).PTEN gene also maps at this lost location and has been consid-ered a relevant gene in the progression of GBM as it controlscell proliferation through the PI3K/Akt pathway. Nevertheless,DNA losses detected at PTEN locus (10q23) did not reach sig-nificance.

In conclusion, we have reported for first time the correspon-dence between DNA copy number changes and gene expressionlevels of relevant genes for the progression and malignancy ofthe tumor. Special interest must be given to genes involved inangiogenesis (ITGA5), ECM matrix remodeling (COL5A2 andCOL1A2), and oncogenes (AXL) as amplified and overexpressedgenes. Further studies are needed to confirm these results andto find out the implication of these genes in the progression ofhigh-grade astrocytomas.

The concordance between the results obtained from bothstrategies is remarkable, as most of the overexpressed genesmapping at amplified chromosomal locations were not randomand could have a direct implication on the tumor progressionprocess.

In this sense, combined analysis of genomic DNA CNVs dataalong with gene expression data may result in an appropriatestrategy to understand tumor pathogenesis as well as to identifyputative candidate genes to be used as diagnostic/prognosticmarkers of the disease or therapeutic targets.

ACKNOWLEDGMENTSThis work was financially supported by grants 9/212.327-

15887 (University of the Basque Country), SAIOTEK (Dpt. In-dustry of the Basque Country); G03/114 Instituto de Salud Car-los III (Spanish Health Ministry) Fundacion Mutua Madrilenaand IT/460/07 (Basque Government).

Olatz Leis is supported by the Health Department of theBasque Country Government.

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