Global Gene Expression Profiling of Circulating Endothelial

Cells in Patients with Metastatic Carcinomas

Denis A. Smirnov, Bradley W. Foulk, Gerald V. Doyle, Mark C. Connelly,Leon W.M.M. Terstappen, and S. Mark O’Hara

Immunicon Corp., Huntingdon Valley, Pennsylvania

Abstract

Increased numbers of endothelial cells are observed inperipheral blood of cancer patients. These circulatingendothelial cells (CECs) may contribute to the formation ofblood vessels in the tumor or reflect vascular damage causedby treatment or tumor growth. Characterization of thesecells may aid in the understanding of the angiogenic processand may provide biomarkers for treatment efficacy ofangiogenesis inhibitors. To identify markers typical for CECsin cancer patients, we assessed global gene expressionprofiles of CD146 immunomagnetically enriched CECs fromhealthy donors and patients with metastatic breast, colorec-tal, prostate, lung, and renal cancer. From the generatedgene profiles, a list of 61 marker genes for CEC detection wasgenerated, and their expression was measured by real-timequantitative PCR in blood samples from 81 metastatic cancerpatients and 55 healthy donors that were immunomagneti-cally enriched for CECs. A set of 34 genes, among whichnovel CEC-associated genes, such as THBD, BST1, TIE1,POSTN1, SELE, SORT1, and DTR , were identified that wereexpressed at higher levels in cancer patients compared withhealthy donors. Expression of the VWF, DTR, CDH5, TIE , andIGFBP7 genes were found to discriminate between cancerpatients and ‘‘healthy’’ donors with a receiver operatingcharacteristic curve accuracy of 0.93. Assessment of theexpression of these genes may provide biomarkers toevaluate treatment efficacy. (Cancer Res 2006; 66(6): 2918-22)

Introduction

Approximately 1.2 trillion endothelial cells in an average adultcover the internal surface of blood and lymphatic vessels andactively participate in many physiologic events, such as vascularpermeability, inflammation, hemostasis, and angiogenesis (1).Consequently, endothelial cells are involved in multiple clinicalpathologic conditions, including cancer, cardiovascular diseases,autoimmune diseases, and infectious diseases (1, 2). Endothelialcells can be found in peripheral blood, the site of origin, andprecise role of these circulating endothelial cells (CECs) are not yetunderstood (3). Increased numbers of CECs have been observed inperipheral blood of patients with a variety of disorders, such asmyocardial infarction, infectious vasculitis, kidney transplant

rejection, and cancer (3, 4). Enumeration and characterization ofCECs may offer a unique opportunity to study the vasculature andimprove our understanding of a variety of disease processes. Incancer, in particular, detection and characterization of CECs maybe useful for monitoring clinical treatment efficacy of antiangio-genesis therapies, such as bevacizumab, SU6668, and others (4, 5).Reverse transcription-PCR (RT-PCR) is widely used for detection

of circulating tumor cells in the peripheral blood (6, 7). To date,however, RT-PCR is used infrequently to study endothelial cells inperipheral blood, which is likely due to the lack of suitable andtested markers (4, 8). Our objective here was to identify markers formonitoring CECs in peripheral blood samples. We profiled geneexpression in CECs enriched from seven cancer patients withhigh number of detected CECs and three ‘‘healthy’’ donors usingmicroarrays, identified candidate marker genes, and validated theirperformance in a broader group of cancer patients and healthydonors using quantitative RT-PCR. As a result of these experiments,we identified a set of marker genes that can be potentially used tomonitor CECs in peripheral blood by quantitative RT-PCR.

Materials and Methods

Patient samples, immunomagnetic sample preparation, and CECenumeration. Blood from cancer patients and healthy volunteers was drawn

into a 10-mL EDTA-containing vacutainer tubes (Becton Dickinson, FranklinLakes, NJ) for RNA extraction and a 10-mL CellSave preservative tube

(Immunicon Corp. Huntingdon Valley, PA) for CEC enumeration. Samples

were kept at room temperature and processed within 36 hours after

collection. Information about all samples used in this study is presentedin Supplementary Tables S1A and S2A online. All participants signed an

Institutional Review Board–approved informed consent form before study

participation. The healthy individuals used for comparison with the patients

had no known illness or fever at the time of draw and had no known history ofmalignant disease. The CellTracks AutoPrep system and the CellSpotter

Analyzer system (Immunicon) were used to enrich and enumerate

endothelial cell from peripheral blood. Four milliliters of collected bloodwere used for CEC enumeration and RNA extraction. Briefly, endothelial cells

were immunomagnetically captured using ferrofluids coated with CD146

antibodies. The enriched cells were then labeled with the nuclear dye 4V,6-diamidino-2-phenylindole (DAPI), CD105 antibodies conjugated to phycoer-ythrin (CD105-PE), and the pan-leukocyte antibody CD45 conjugated to

allophycocyanin (CD45-APC). The CD146-enriched, fluorescently labeled

cells were identified as CECs when the cells exhibited the DAPI+/CD105+/

CD45� phenotype (3). For RNA, endothelial cells were immunomagneticallycaptured using ferrofluids coated with CD146 antibodies while the

fluorescent labeling was omitted.

RNA isolation, target preparation, microarrays hybridization, andmicroarray data analysis. For gene expression studies following

immunomagnetic enrichment, CECs were lysed by adding 100 AL of Trizol

reagent (Invitrogen, Carlsbad, CA). RNA from all samples was isolated using

Trizol reagent, DNase I treated, and Trizol repurified. Ten nanograms oftotal RNA from the CEC-enriched fraction from each of the seven cancer

patients with high CEC counts and three ‘‘healthy’’ donors were used to

prepare biotinylated hybridization targets with Affymetrix’s eukaryotic

Note: Supplementary data for this article are available at Cancer Research Online(http://cancerres.aacrjournals.org/).

Microarray gene expression data was deposited to MIAME Express database http://www.ebi.ac.uk/miamexpress/ (submission no. MIAMEXPRESS# E-MEXP-476).

Request for reprints: Leon Terstappen, Immunicon Corp., 3401 Masons Mill Road,Suite 100, Huntingdon Valley, PA 19006. Phone: 215-830-0777; Fax: 215-830-0751;E-mail: lterstappen@immunicon.com.

I2006 American Association for Cancer Research.doi:10.1158/0008-5472.CAN-05-4003

Cancer Res 2006; 66: (6). March 15, 2006 2918 www.aacrjournals.org

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small sample target labeling assay, version II.1 Biotinylated target cRNA wasthen hybridized to an Affymetrix Focus array containing >8,500 verified

human sequences according to manufacturer’s instructions, and gene

expression data were obtained using the Affymetrix Microarray Analysis

Suite, version 5.0. A global scaling normalization procedure to normalizethe expression data to the target value of 150 was done. More detailed

information about the microarray experiments is available online

(submission no. MIAMEXPRESS# E-MEXP-476).2

Multigene quantitative real-time RT-PCR analysis. To narrow downthe list of candidate genes for the real-time RT-PCR verification studies, we

focused on the genes with high ratios between median expression intensity

value from cancer patients and ‘‘healthy’’ donors and minimal expression in

leukocytes based on data published in the Cancer Gene Anatomy ProjectSAGE database.3 We also measured expression levels of a few well-

characterized endothelial markers whose expression was not detectable

using Affymetrix chips (e.g., CDH5) or that showed minimal differencesbetween CEC-enriched samples from cancer patients and healthy donors

(e.g., VCAM1, CD34 , and SELE). Expression of the selected candidate marker

genes was evaluated in a separate set of metastatic cancer patients and

a control group of healthy volunteers. To ensure that a sufficient amount ofcDNA was available for multigene analysis, the RNA extracted from the

CEC-enriched fraction of each blood sample was subjected to one round of

amplification using the MessageAmp aRNA kit (Ambion, Austin, TX)

according to the manufacturer’s instructions. A total of 25 ng of theresulting aRNA was reverse transcribed. cDNA was diluted 30-fold with

distilled water, and then a volume of 10 AL of the cDNA samples was used in

each RT-PCR reaction. Where possible, primer sequences that amplifieda product of about 100 bp within 300 bases of the 3V-end of the transcript

were selected (see Supplementary Table S2B). Quantitative RT-PCR was done

using the SYBR Green PCR Master Mix and an ABI Prism 7000 Detection

System (Applied Biosystems, Foster City, CA). Gene expression levels weredetermined using a standard calibration curve prepared from gene-specific

RT-PCR products with known concentrations. Gene expression levels

between samples were normalized using the expression levels of the

ribosomal protein RPS27A gene, which was shown recently to exhibit theleast amount of variability in expression levels among different tissues (9).

Statistical analysis of gene expression data. To allow logarithmic

transformation of the negative observations (zero transcripts detected) in thequantitative real-time PCR data, a constant c = 2 was added to all measured

number of transcripts for all genes in all clinical samples (10). To present

expression data (obtained bymicroarrays or by quantitative RT-PCR) for each

gene in each profiled sample, log 2–transformed ratios between expressionvalues in tested samples and the median expression value between cancer

patients and healthy donors were calculated. Heat maps were constructed

using theMultiple Experiment Viewer software (11). Gene expression levels in

the CTC-enriched fraction of the blood samples from the set of metastaticcancers and the normal donors were compared using the Mann-Whitney

test to identify genes with significantly different expression levels (see

Supplementary Table S2C and D). Potential diagnostic value of each gene

and their combination was estimated using an area under the receiveroperating characteristic curve (AUC ROC).

Results

Generation of global expression profiles for CECs frompatients with metastatic cancers. An integrated samplepreparation system was used to enrich CECs from 4 mL of blood

Figure 1. Results of global CEC expression profiling by microarrays.A, fold differences between median (median ratio) and mean (mean ratio)expression values for samples from cancer patients and healthy donors arepresented for each gene in the form of a heat map (black to yellow scale).B, expression data for each gene in each profiled sample are presented in theform of a heat map (green to red scale) of log 2–transformed ratios betweenexpression values in tested sample and the median expression value betweencancer patients (ReCa, renal cancer; PrCa, prostate cancer; LuCa, lung cancer;CoCa, colorectal cancer; BrCa, breast cancer) and healthy donors (HlDo ).

Global Gene Expression Profiling of CECs

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using magnetic nanoparticles conjugated to monoclonal antibodiesagainst CD146 expressed on endothelial cells and a subset ofactivated T lymphocytes. Despite a 10,000-fold enrichment, CECsare still outnumbered by carried over leukocytes and the subset ofactivated T lymphocytes. From previous spike-in experiments, itwas estimated that a considerable number of genes expressed incirculating cells could be identified with the Affymetrix GeneChipsystem iff50 to z100 captured cells were present in a backgroundof f1,000 to 10,000 leukocytes (data not shown). Based on CECenumeration information, we selected CEC-enriched samples fromseven cancer patients with high number of CECs (renal cancer, 102CECs; prostate cancer, 87 CECs; lung cancer, 88 CECs; colorectalcancer, 557 CECs; breast cancer, 81 CECs; breast cancer, 299 CECs;breast cancer, 118 CECs; Supplementary Table S1A). To identifypotential markers for detection and characterization of carcinomaassociated CECs, gene expression profiles from CEC-enrichedsamples from abovementioned seven cancer patients werecompared with CEC-enriched samples from three ‘‘healthy’’ donors(containing 1, 43, and 48 detected CECs; Supplementary Table S1A).After a global scaling procedure was used to normalize theexpression data between experiments, we selected f160 genes aspotential markers for detection of CECs in peripheral blood (Fig. 1;Supplementary Table S1A-B). We concentrated on the genes withhigh ratios between median expression values in cancer samplesand median expression values in ‘‘healthy’’ donors (Fig. 1A). Amongthe genes more abundant in CEC-enriched samples from cancerpatients were a number of genes known to be associated withendothelial function. This list includes tissue inhibitor of metal-loproteinase (TIMP2), a protein involved in regulation of angio-genesis; thrombomodulin (THBD), an endothelial cell surfaceglycoprotein; endoglin (ENG or CD105), a membrane glycoproteinprimarily associated with human vascular endothelium; adreno-medullin (ADM), a potent vasodilator and a hypotensive agent;vascular endothelial growth factor (VEGF), a well-known mitogenfor vascular endothelial cells; and CD146 , a gene that encodesendothelial cell adhesion protein that was used to capture CECsfrom peripheral blood (3, 12–15). These findings indicate that geneexpression profiling of CECs can be a useful tool for identificationof potential marker genes for CEC detection.Verification of CEC-specific expression of the candidate

genes by quantitative real-time RT-PCR. It was crucial toconfirm that the global expression profiles generated from CECsenriched from peripheral blood of the seven cancer patients arereflective of CEC gene expression signatures in a larger populationof patients. Therefore, expression of the candidate genes selectedfrom the microarray analyses was measured in the CEC-enrichedblood fractions from 81 metastatic colorectal cancer patients anda control group of 55 apparently healthy normal donors usingquantitative real-time RT-PCR (see Supplementary Table S2A).Consistent with previous reports (4), we observed statisticallysignificant difference (P < 0.05, Mann-Whitney test) in frequenciesof CECs in peripheral blood of patients with metastatic cancer and‘‘healthy’’ volunteers. In blood samples from 55 ‘‘healthy’’ donors,2 to 378 CECs/4 mL were present (mean = 29, median = 13),whereas in 62 samples from metastatic cancer patients, CECsranged from 1 to 1,560 (mean = 70, median = 26).

Figure 2. Results of CEC expression profiling in set of samples fromcancer patients and healthy donors by quantitative RT-PCR. A, folddifferences between median (median ratio) and mean (mean ratio) expressionvalues between samples from cancer patients and healthy donors for eachgene are presented in the form of a heat map (black to yellow scale). B,expression data for each gene in each profiled sample are presented in theform of a heat map (green to red scale) of log 2–transformed ratios betweenexpression values in tested samples and the median expression valuebetween cancer patients and healthy donors. C, values of AUC ROC foreach gene.

1 http://www.wi.mit.edu/CMT/Protocols/AffySmlSamplProto.pdf.2 http://www.ebi.ac.uk/miamexpress/.3 http://cgap.nci.nih.gov/SAGE/AnatomicViewer.

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For quantitative RT-PCR studies, 61 genes were selected. Inaddition to genes selected from the microarray experiments, wealso tested a small number of well-characterized endothelialmarkers whose expression was not detectable using Affymetrixchips (e.g., CDH5) or that showed minimal differences betweenCEC-enriched samples from cancer patients and healthy donors(e.g., VCAM1, CD34 , and SELE ; Supplementary Table S1B). Amongthe 61 genes tested by quantitative RT-PCR, transcripts for 34 geneswere more abundant (P < 0.01, Mann-Whitney test) in CEC-enriched samples from 81 metastatic cancer patients than in55 ‘‘healthy’’ donors (Supplementary Table S2A). Figure 2 depictsexpression data for these 34 genes that show greater numberof transcripts detected in CEC-enriched samples from patientswith metastatic cancer when compared with CEC-enrichedsamples from ‘‘healthy’’ control donors. Among the genes thatare more abundant in CEC-enriched samples from cancer patientsare several with well-characterized endothelial functions. This listincludes von Willebrand factor (VWF), THBD, TIMP2 , proteinreceptor tyrosine kinase (TIE), and its family member TIE2 (TEK ;refs. 12, 16). Among the genes with less studied roles in endothelialcells are periostin (POSTN), osteonectin (SPARC), diphtheria toxinreceptor (DTR or HB-EGF), neuregulin 1 (NRG1), bone marrowstromal cell antigen 1 (BST1), sortilin (SORT1), and others.Interestingly, recent reports identified several of these genes,including POSTN, SPARC, DTR , and NGR1 , as potent promoters ofangiogenesis and enhancers of tumor growth (17–20). Theseobservations suggest that differences in gene expression observedbetween CEC-enriched samples from cancer patients and ‘‘healthy’’donors may be associated with active processes of angiogenesisand tumor growth in cancer patients.Among the cancer samples that were used to validate expression

of potential markers by quantitative real-time PCR, there were 12samples from colorectal cancer, 22 samples from lung cancer, and29 samples from breast cancer patients (Supplementary Table S2A).When we compared expression pattern for the 34 genes that weremore abundant in CEC-enriched samples from cancer patients thanin ‘‘healthy’’ donors, we observed no statistically significantdifference in gene expression profiles between patients withcolorectal, lung, and breast cancer (Kruskal-Wallis test, a = 0.01;Supplementary Table S2E).Discrimination between patients with metastatic cancers

and ‘‘healthy’’ donors using real-time RT-PCR gene expressiondata. It was reported that the levels of CECs correlate well withthe degree of tumor angiogenesis or the response to angiogenictherapy (5). Moreover, recent reports also indicate that high plasmaconcentrations of endothelial cell–derived proteins (e.g., vWF andVCAM1) correlate with advanced diseases and significantly poorprognosis of patients with metastatic carcinomas (21, 22). Theseobservations suggest that quantitative real-time RT-PCR analysismay also have clinical use for the characterization of the CECs.To estimate potential use of identified marker genes to discriminatebetween cancer patients and ‘‘healthy’’ donors, we calculated theAUC ROC values for all the 34 genes that were more abundant

in CEC-enriched samples from cancer patients than in ‘‘healthy’’donors (Fig. 2C). AUC ROC values are commonly used as a summarymeasure of diagnostic accuracy (23). AUC ROC values for measuredgenes varied from the lowest of 0.64 ( for TNS) to the highest of 0.88( for VWF ; Fig. 2C ; Supplementary Table S2D) and exceeded for mostgenes the AUC ROC value of 0.65 calculated strictly based on thenumber of CECs detected in tested clinical samples. From a numberof gene expression studies, it is clear that the use of multiple markersusually improves the accuracy (24). Indeed, a simple combination ofgenes with the highest AUC ROC values (VWF, DTR, CDH5, TIE ,and IGFBP7) was estimated to have an AUC ROC value of 0.93, whichis considered to be an excellent classification accuracy (25). Thissuggests that just a few markers can be used to develop a highlyaccurate RT-PCR based test to characterize CECs.

Discussion

Little is known about molecular characteristics of the CECs thatare detected in peripheral blood of cancer patients. To identify a setof genes that can be used for detection and characterization of CECsin peripheral blood, we first compared gene expression profiles fromCEC-enriched samples from the blood of patients with metastaticbreast, colorectal, prostate, lung, and renal cancers with CEC-enriched samples from three ‘‘healthy’’ donors. Expression of the61 candidate genes was then measured by quantitative real-timePCR in the CEC-enriched blood fractions from 81 metastatic cancerpatients and a control group of 55 apparently healthy normaldonors. Transcripts for the 34 genes were significantly moreabundant in CEC-enriched samples frommetastatic cancer patientsthan in ‘‘healthy’’ donors. Among these genes several have well-known association with endothelial function (e.g., VWF, TIE , andCDH5). Others genes (e.g., POSTN, SPARC, DTR , and NGR) wereshown to be involved in angiogenesis and tumor growth. Theseresults indicate that the differences between CEC-enriched samplesfrom cancer patients and ‘‘healthy’’ donors may be indicative of theactive processes of angiogenesis and tumor growth in cancerpatients. We also identified several genes whose role in vascularfunction and angiogenesis is yet to be elucidated. This list includesBST1, SORT1, TNS , and others. Our analysis of ROC curves suggeststhat quantitative real-time RT-PCR analysis of samplesimmunomagnetically enriched for CECs can be used to accuratelydetect and characterize endothelial cells found in the blood ofpatients with metastatic cancer and potentially other diseases. Webelieve that global expression profiles of CECs may provide insightsthat could improve our understanding of endothelial function incancer and could lead to the development of both novel noninvasivediagnostic tools as well as novel therapeutic targets.

Acknowledgments

Received 11/8/2005; revised 12/22/2005; accepted 1/20/2006.Grant support: Immunicon Corp.The costs of publication of this article were defrayed in part by the payment of page

charges. This article must therefore be hereby marked advertisement in accordancewith 18 U.S.C. Section 1734 solely to indicate this fact.

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