Gene Expression Proﬁle in Response to Doxorubicin...

Preclinical Development

Gene Expression Profile in Response to Doxorubicin–Rapamycin Combined Treatment of HER-2–OverexpressingHuman Mammary Epithelial Cell Lines

Adriana Priscila Trap�e1, Maria Lucia Hirata Katayama1, Rosimeire Aparecida Roela1, Helena Brentani2,Graziela Rosa Ravacci3, Leandro de Araujo Lima4, and Maria Mitzi Brentani1

AbstractHER-2–positive breast cancers frequently sustain elevated AKT/mTOR signaling, which has been associated

with resistance to doxorubicin treatment. Here, we investigated whether rapamycin, an mTOR inhibitor,

increased the sensitivity to doxorubicin therapy in two HER-2–overexpressing cell lines: C5.2, which was

derived from theparentalHB4aby transfectionwithHER-2andSKBR3,whichexhibitsHER-2 amplification.The

epithelial mammary cell line HB4a was also analyzed. The combined treatment using 20 nmol/L of rapamycin

and 30 nmol/L of doxorubicin arrested HB4a and C5.2 cells in S to G2–M, whereas SKBR3 cells showed an

increase in the G0–G1 phase. Rapamycin increased the sensitivity to doxorubicin in HER-2–overexpressing cells

by approximately 2-fold, suggesting that the combination displayed a more effective antiproliferative action.

Gene expression profiling showed that these results might reflect alterations in genes involved in canonical

pathways related to purine metabolism, oxidative phosphorylation, protein ubiquitination, and mitochondrial

dysfunction. A set of 122 genes modulated by the combined treatment and specifically related to HER-2

overexpression was determined by finding genes commonly regulated in both C5.2 and SKBR3 that were not

affected in HB4a cells. Network analysis of this particular set showed a smaller subgroup of genes in which

coexpression pattern in HB4a cells was disrupted in C5.2 and SKBR3. Altogether, our data showed a subset of

genes that might be more robust than individual markers in predicting the response of HER-2–overexpressing

breast cancers to doxorubicin and rapamycin combination. Mol Cancer Ther; 1–11. �2011 AACR.

Introduction

HER-2/neu, amemberof theErbB receptors, is amplifiedand/oroverexpressed in about 25% to30%ofhumanbreastcancers and generally correlates with poor prognosis (1).Although it has been used in the clinical practice as aparameter to indicate those patients who would betterbenefit from trastuzumab treatment, almost all HER-2–positive tumors develop resistance to this monotherapy(2). Controversial data arise out of the predictor role ofthis receptor in response to conventional chemotherapy,

because it has been described as conferring either sensiti-vity (3–5), relative resistance (6–8), or exerting no influences(9) on anthracycline-based regimes, including doxorubicin.

Doxorubicin intercalates in theDNAdouble strand andforms a stable complex with the enzyme topoisomerase II(TOP2A), the direct target of anthracyclines. The fact thatHER-2 and TOP2A genes are frequently coamplified inbreast cancers led some researchers to speculate the pre-dictive value of these simultaneous alterations (10–12).One mechanism of resistance to doxorubicin-inducedapoptosis in HER-2–positive tumors seems to be theability of this receptor to activate several cell survivalpathways, such as the phosphoinositide 3-kinase (PI3K)/Akt/mTOR signaling,which can induce cell proliferation,transformation, and increased cell motility (13). More-over, microarray analysis of breast tumor biopsiesshowed significant correlation betweenTOP2Aandgenesinvolved in mTOR pathway (14).

Considering the role of PI3K/AKT/mTOR signaling inthe acquisition of resistance to the antitumoral effects ofchemotherapy, it seems reasonable to investigatewhetherblockade of this pathway could increase the response ofbreast cancer cells to the antitumoral effects of doxorubi-cin. Previously published data obtained from differentcancer models have shown optimized results after thecombination of several chemotherapeutic agents with

Authors' Affiliations: 1Departamento de Radiologia e Oncologia, LIM24,Faculdade de Medicina, Universidade de S~ao Paulo; 2Departamento dePsiquiatria, Faculdade de Medicina, Universidade de S~ao Paulo; 3Depar-tamento de Gastroenterologia, LIM35, Faculdade de Medicina, Universi-dade de S~ao Paulo; and 4Laborat�orio de Bioinform�atica e Bioestat��stica,Centro Internacional de Pesquisa e Ensino (CIPE) - Hospital AC Camargo,S~ao Paulo, Brazil

Note: Supplementary material for this article is available at MolecularCancer Therapeutics Online (http://mct.aacrjournals.org/).

Corresponding Author: Maria Mitzi Brentani, Faculdade de Medicina daUniversidade de S~ao Paulo, Departamento de Radiologia e Oncologia,AvenidaDr. Arnaldo, 455, 4� andar, sala 4115, CEP: 01246-903, S~aoPaulo,SP, Brazil. Phone: 55-11-3082-6580; Fax: 55-11-3082-6580; E-mail:[email protected]

doi: 10.1158/1535-7163.MCT-11-0033

�2011 American Association for Cancer Research.

MolecularCancer

Therapeutics

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on May 21, 2018. © 2011 American Association for Cancer Research. mct.aacrjournals.org Downloaded from

Published OnlineFirst November 14, 2011; DOI: 10.1158/1535-7163.MCT-11-0033

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PI3K/AKT/mTOR inhibitors, such as rapamycin (14–15).A more recent study showed clinical benefits from met-astatic breast cancer patients treated with trastuzumabassociated with everolimus, a rapamycin analog (16).

Rapamycin inhibits specifically the mTOR protein,leading to dephosphorylation of its main downstreamtargets, p70S6K and 4EBP1, which results in the suppres-sion of translation of mRNAs related to cell cycle andproliferation (17). Despite preclinical studies havedescribed favorable effects of rapamycin on differentbreast cancermodels, clinical trials showed only amodestantitumoral activity in patients, which may be attributedto the fact that this drug is essentially cytostatic and notcytotoxic as chemotherapeutic agents (18, 19).

Altogether, these evidences highlight the relevance ofcombining rapamycin with doxorubicin to improve theresponse of HER-2–positive tumors to both drugs. Nev-ertheless, the molecular mechanisms involved in thistreatment need to be elucidated to provide insights aboutthe biological processes modulated by the combination.Therefore, we aimed to investigate whether rapamycincould increase the sensitivity of HER-2–expressing breastepithelial cell lines to doxorubicin. Gene expression pro-file generated from this approach might lead to a betterunderstanding about the molecular mechanisms associ-ated with the response of HER-2-overexpressing tumorsto the combined treatment.

Materials and Methods

ReagentsRapamycin (Calbiochem) and doxorubicin hydrochlo-

ride (Sigma-Aldrich) were solubilized in dimethyl sulf-oxide (DMSO) and stored at�20�C.Propidium iodide (PI)was purchased from Sigma-Aldrich and Cell Titer Glowas purchased from Promega. Chemical structures ofrapamycin and doxorubicin are presented in Supplemen-tary Fig. S1.

Cell cultureThe HB4a and C5.2 cell lines were kindly provided in

2006 by Dr. Michael O’Hare from Ludwig Institute forCancer Research, London, United Kingdom. The SKBR3cells from the American Type Culture Collection werekindly provided in 2009 by Dr. A.A. Camargo from Lud-wig Institute for Cancer Research, S~ao Paulo Branch,Brazil. The C5.2 cells were established by transfectionof parental mammary luminal epithelial HB4a cells withcDNAfull-lengthcorresponding toHER-2gene (20).HB4aexpresses basal levels of HER-2 receptor, and C5.2expresses high levels of this receptor similarly to SKBR3.The cellswere cultured as described elsewhere (21), testedperiodically for Mycoplasm contamination, and authenti-cated by quantitative real-time reverse transcriptase (RT)-PCR to evaluateHER-2 overexpression inC5.2 and SKBR3compared with HB4a cells (data not shown). The 3 celllines express EGFR (21) and are negative for estrogenreceptor and progesterone receptor (data not shown).

TreatmentsCells were seeded in 25 cm2 flasks or 96-well culture

platesat adensityof 5� 103 cells/cm2 to15� 103 cells/cm2.Flow cytometry and luminescence assays were con-ducted after 24 and 72 hours of treatment, respectively.Cells were treated with DMSO (0.04% v/v), 20 nmol/Lof rapamycin, and 30 to 1,000 nmol/L of doxorubicin.The combined treatment was carried out by adding afixed dose of rapamycin (20 nmol/L) to each doxoru-bicin dose. Microarray experiments and Western blot-ting were carried out after 24 hours of treatment with30 nmol/L of doxorubicin and 20 nmol/L of rapamycin,doses clinically achieved in plasma of patients under-going these therapies (22, 23).

Flow cytometry and cell viability assaysCell-cycle distribution was determined by a FACS-

Calibur flow cytometer (Beckton Dickinson) after PIincorporation. Cell viability was measured with CellTiter Glo Reagent. GraphPad (version 5.0) softwarewas used for statistical analyses, carried out by ANOVAand Tukey post test. Dose–response curves were con-structed by nonlinear regression analyses (P < 0.05).

Western blottingProteins were extracted in lysis buffer (50 mmol/L

Tris-HCl, pH 7.4; 150 mmol/L NaCl; 1% NP-40; 0.5%sodium deoxycholate; 0.1% SDS; and protease inhibi-tors) and the concentration was determined using theBradford Protein Assay Reagent. Proteins (40 mg) wereboiled for 5 minutes in Laemli buffer, separated by 12%SDS-PAGE, and transferred to nitrocellulose mem-branes, which were blocked for 1 hour in 5% skimmedmilk in TBS-T (0.05% Tween-20) and incubated withmonoclonal antibodies (1:500) against human p-AKT(Thr308), p-p70S6K (Thr389), p-4EBP1 (Thr37/46) andtotal proteins Akt, p70S6K, and 4EBP1 (Cell SignalingTechnology) for 16 hours at 4�C. Following incubationwith antirabbit immunoglobulin (1:3,000), the resultswere detected with ECL Western blotting (GE Health-care) and quantitative densitometric analysis of thebands was conducted by Scion Image Software.

Microarray proceduresTotal RNA was isolated with TRIzol reagent (Invi-

trogen) and purified by the RNeasy Mini Kit (QIA-GEN). The RNA quality and purity were analyzed byNanoDrop ND-1000 (Thermo Scientific) and Agilent2100 Bioanalyzer (Agilent). Only total RNA sampleswith 260/280 ratios between 1.9 and 2.0 and RNAintegrity number more than 7 were further processed.Total RNA (250 ng) from samples were amplified by theGeneChip 30IVT Expression Kit Assay and hybridizedto Human Genome U133 Plus 2.0 GeneChips. Afterhybridization, genechips were scanned with a Gene-Chip Scanner 3000 7G and data were recovered usingExpression Console Software (Affymetrix). All experi-ments presented the indexes of quality control as

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specified in the Affymetrix guidelines for assessingsample and array quality.

Gene expression analysisThe data were normalized with RMA Express Release

1.0 software. Statistical analyseswere conductedbyMulti-Experiment Viewer v.4.5 (Mev) software. Differentiallyexpressed genes were identified after comparing eachtreatment to the control cells by SAM method usingcriteria based on FDR less than 5%. The genes wereassigned to canonical pathways and functions by Inge-nuity Pathway Analysis software (P < 0.05).

Network constructionNetwork analysis was carried out for the 122 genes

related to HER-2–mediated response to the combinedtreatment, based on the interactome database (humanprotein data base, HPRD; ref. 24). For each cell line(HB4a, C5.2, and SKBR3), we selected the shortest path-ways between 2 nodes and the first neighbors. Next, theR software (25) was used to calculate Pearson correla-tion coefficient (PCC) among normalized expressionvalues of genes in each sample. PCC values of bothC5.2 and SKBR3 cells were compared with HB4acells and we selected for further investigation only thosegene pairs that displayed differences greater than 1.5.Data analysis and visualization were conducted byCytoscape software (version 2.8.0; www.cytoscape.org).

Quantitative real-time RT-PCRValidation experiments were carried out for 11 out of

122 genes related to HER-2–mediated response to thecombined treatment. Samples of C5.2 and SKBR3 cellswere reverse transcribed with 2 mg of total RNA, randomprimers (Promega Corporation), and Superscript IIIReverse Transcriptase (Invitrogen Corporation). Primerswere designed with Primer-Blast software (http://www.ncbi.nlm.nih.gov/tools/primer-blast/) and synthesizedby IDT (IntegratedDNATechnologies). Real-time RT PCRreactions were carried out in duplicate using SYBR GreenPCR Power MasterMix in the 7900HT Fast Real TimeSystem (Applied Biosystems) with the following thermalcycling: 95�C (10 minutes) and 40 cycles of 95�C (15 sec-onds) and 60�C (1 minute). b-Actin (ACTB) gene expres-sion was used as endogenous control, and a pool of C5.2and SKBR3 cells was used as calibrator for calculating

relative expression of target genes with 2�DDCt method.Statistical analyses were carried out with Student’s t test.

Results

Effects of doxorubicin and rapamycin on cell-cycledistributionThe effects of doxorubicin and rapamycin on cell-

cycle distribution were determined by flow cytometry.The HB4a cells showed a remarkable 50% increase incells situated in S to G2–M (P < 0.001) by doxorubicin,because the percentage of cells in this phase shifted

from 26.8% in control cells to 74.6% after exposure toa 30 nmol/L dose. Increased doses of doxorubicinshowed no significant alterations in this cell-cycle dis-tribution up to 250 nmol/L dose (P > 0.05). Neverthe-less, higher concentrations (500–1,000 nmol/L) resultedin depletion of cells in S to G2–M, which could beexplained by an increase in the percentage of cells inG0–G1 and in sub-G1 (P < 0.05; Fig. 1A). The C5.2 cellsseemed to be more resistant to the effects of doxorubicinon S to G2–M arrest, because these cells displayed 50%increase in S to G2–M only after exposure to 120 nmol/Ldose (P < 0.01). Although these results were maintainedup to 500 nmol/L of doxorubicin, the 1,000 nmol/L dosereduced the percentage of cells in the S to G2–M phase,with a concomitant increase in G1 phase (P < 0.01;Fig. 2A). The ability of doxorubicin to induce S toG2–M arrest was less prominent in SKBR3 cells com-pared with the others. The maximum increase in thepercentage of cells in S to G2–M phase consisted in 30%and occurred after treating cells with a 120 nmol/L dose(P < 0.001; Fig. 3A). We observed no substantial celldeath as shown by absence of peaks in sub-G1 phase forC5.2 and SKBR3, even at higher doses (P > 0.05).

The effects of the combined treatment on cell-cycledistribution were similar to those generated by doxo-rubicin alone in HB4a and C5.2 cells, because theincrease in S to G2–M also occurred after exposureto 30 nmol/L doxorubicin and was only 10% smallerafter the combined treatment than doxorubicin alone(Figs. 1B and 2B). These effects were attenuated afterincreasing the doses of the chemotherapeutic agent.In SKBR3 cells; the response to the combined treat-ment was different from that with doxorubicin alone.Rapamycin seemed to cause an increase of 10% ofcells in G0–G1 phase with a concomitant decrease inS to G2–M after its addition to 30 nmol/L of doxoru-bicin (P < 0.05). Increased doxorubicin doses resultedin a cell-cycle distribution similar to that of control cells(P > 0.05; Fig. 3B).

Although 20nmol/Lof rapamycin alone causedno cell-cycle alterations in HB4a cells compared with the control(P > 0.05), C5.2 and SKBR3 cells displayed 12% and 15%increase, respectively, in the percentage of cells in G0–G1

(P < 0.01; Figs. 1C, 2C, and 3C). These increases wereaccompanied by a depletion of cells accumulated in S toG2–M. Increaseddoses of rapamycin causedno changes incell-cycle distribution for all cell lines studied (data notshown).

Effects of doxorubicin and rapamycin on cell growthDoxorubicin inhibited cell growth of the 3 cell lines in a

dose-dependent manner (Fig. 4). The results showed that30 nmol/L doxorubicin caused a 35% inhibition in cellgrowth ofHB4a andC5.2 cells, reaching 90% to 100% afterexposure to doses ranging from 250 nmol/L to 1,000nmol/L (P < 0.01). According to the flow cytometricresults, SKBR3 cells seemed to be more resistant to theantiproliferative effects of doxorubicin, which consisted

Genes Affected by Rapamycin–Doxorubicin in Mammary Cells

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in 23% of growth inhibition at a 30 nmol/L dose (P < 0.01).Escalating doses of doxorubicin caused 77%of cell growthinhibition, reaching up to 90% at 1,000 nmol/L. Rapamy-cin caused 35% growth inhibition in HB4a cells, 47% inC5.2 cells, and 40% in SKBR3 cells (P < 0.001; data notshown).

The addition of rapamycin was 20% more effective ininhibiting cell growth than doxorubicin alone, parti-cularly after adding rapamycin to 30 to 120 nmol/Ldoxorubicin for HB4a (P < 0.05) and C5.2 cells (P < 0.001)and to 30 nmol/L for SKBR3 cells (P < 0.01). Higherconcentrations of doxorubicin had similar effects tothose exerted by this agent alone (P > 0.05; Fig. 4).Nonlinear regression analyses showed that rapamycinwas able to reduce about 2-fold the IC50 of doxorubicin(P < 0.0001), which changed from 54.2, 56.3, and 88.2nmol/L to 23.4, 22.9, and 47.1 nmol/L for HB4a, C5.2,and SKBR3, respectively (Supplementary Fig. S2).Moreover, the higher IC50 values of SKBR3 cells sug-gested that these cells were more resistant to doxoru-bicin than the other cell lines.

Impact of rapamycin, doxorubicin, and thecombination of these two drugs on thephosphorylation level of AKT, p70S6K, and 4EBP1

Determination of the impact of rapamycin, doxoru-bicin, and their combination on activation of the mTORpathway was carried out in HB4a, C5.2, and SKBR3cells. We analyzed the effects of each treatment onphosphorylation levels of AKT protein (upstream reg-ulator), p70S6K, and 4EBP1 (downstream targets ofmTOR; Supplementary Fig. S3). Constitutive levels ofphosphorylated proteins were observed for controlsamples of all cell lines and, compared with the others,SKBR3 cells showed about 2-fold increased levels for p-AKT and p-4EBP1 proteins. After 24 hours of treatment,Western blotting analysis detected no changes in thelevels of p-AKT by each drug individually or the com-bined therapy in HB4a and SKBR3 cells. On the con-trary, C5.2 cells showed increased levels of p-AKT onlyin response to the combined treatment. The levels ofp-p70S6K were significantly decreased by all treatmentsin the 3 cells, mainly after treatment with rapamycin

Figure 1. Cell-cycle distribution ofHB4a cells treated with varyingdoses of doxorubicin (30–1,000nmol/L) and/or a fixed dose ofrapamycin (20 nmol/L).Representative DNA histograms ofcells treated with doxorubicinalone (A), combination of bothdrugs (B), and rapamycin alone (C).Results are expressed as mean of3 independent assays. Values ofSEM were less than 5% (data notshown).

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and its association with doxorubicin. Only SKBR3 cellsdisplayed decreased levels of p-4EBP1 by rapamycinand combination therapies.

Gene expression profilingGene expression profiles were obtained for HB4a, C5.2,

and SKBR3 cells treated with doxorubicin, rapamycin,and the association of both drugs. Figure 5 is a Venndiagram showing that doxorubicin consistently upregu-lated global gene expression and this response was moreremarkable in HB4a cells (n ¼ 1,453) than C5.2 (n ¼ 468)and SKBR3 (n ¼ 174). Repressed genes by doxorubicintreatment were also observed predominantly in HB4acells (n ¼ 231) compared with C5.2 (n ¼ 12) and SKBR3(n ¼ 11).Unlike doxorubicin, rapamycin substantially downre-

gulated global gene expression and these effects occurredpredominantly in C5.2 (n ¼ 1,589) and SKBR3 (n ¼ 867)comparedwithHB4a cells (n¼ 167). The induction of geneexpression by rapamycin occurred prevalently in SKBR3

cells (n ¼ 518 genes) compared with HB4a (n ¼ 190) andC5.2 (n ¼ 188).

Genes deregulated by the combined treatment werepredominantly upregulated in HB4a cells (1,439 up vs.821 down), downregulated in C5.2 cells (509 up vs. 1,203down), and altered in both directions in SKBR3 (926up vs. 1,109 down). Altogether, up- and downregulatedgenes showed a high percentage (40%–50%) of genesexclusively regulated by the combined treatment ofHB4a cells (1,129 of 2,260), C5.2 cells (656 of 1,712), andSKBR3 (1,023 of 2,035). Overlapping genes between eachdrug alone and the combined treatment showed thatthe contribution of rapamycin and doxorubicin on geneexpression consisted mainly in decrease by the formerand upregulation by the latter.

Canonical pathways modulated by the combinedtreatment

To better understand the molecular mechanismsinvolved in the response to the association between the

Figure 2. Cell-cycle distribution ofC5.2 cells treated with varying dosesof doxorubicin (30–1,000 nmol/L)and/or a fixed dose of rapamycin (20nmol/L). Representative DNAhistograms of cells treated withdoxorubicin alone (A), combinationof both drugs (B), and rapamycinalone (C). Results are expressed asmean of 3 independent assays.Values of SEM were less than 5%(data not shown).






2 drugs, differentially expressed genes by the combinedtreatment were classified in canonical pathways. Theseannotations included up- and downregulated genesrepresenting 3 gene sets: (i) overlapped with doxorubicinalone, (ii) overlapped with rapamycin alone, and (iii)exclusively regulated by the combined treatment. Thecomplete list of genes referred to the canonical pathwaysfound in each cell line is available in SupplementaryTables S1A, S1B, S2A, S2B, S3A, and S3B.

Ingenuity Pathway Analysis of the transcripts differ-entially regulated in C5.2 cells by the combined treatmentrevealed that downregulated genes were involved inpathways such as aminoacyl-tRNA biosynthesis, glycol-ysis/gluconeogenesis, pyrimidine and purine metabo-lism, cell-cycle control of chromosomal replication, mito-chondrial dysfunction, and oxidative phosphorylation.

Genes upregulated fell into the following most signifi-cant pathways: PI3K signaling in B lymphocytes, p53and ATM signaling, G2–M DNA damage checkpointregulation, antigen presentation, and NF-kB signaling.

Some pathways in C5.2 cells were also regulated by thecombination in the parental HB4a, such as glycolysis.Nevertheless, we decided to focus on canonical pathwayscommonly regulated in both C5.2 and SKBR3 and notevidenced in HB4a cells to identify those related to theHER-2–mediated response to the combined treatment.Among these pathways, we found PI3K signaling in Blymphocytes, mitochondrial dysfunction, protein ubiqui-tination, oxidative phosphorylation, cyclins and cell-cycleregulation, ubiquinone biosynthesis, and citrate cycle,which, therefore,mightunderlie somemolecularmechan-isms of this response (Supplementary Fig. S4A).

Figure 3. Cell-cycle distribution ofSKBR3 cells treated with varyingdoses of doxorubicin (30–1,000nmol/L) and/or a fixed dose ofrapamycin (20 nmol/L).Representative DNA histograms ofcells treated with doxorubicinalone (A), combination of bothdrugs (B), and rapamycin alone (C).Results are expressed asmean of 3independent assays. Values ofSEM were less than 5% (data notshown).

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Genes exclusively regulated by the combined treatmentmight play relevant roles in the HER-2–mediatedresponse as they were altered irrespective of the actionof each drug alone (Supplementary Fig. S4B). According-ly, a significant percentage of genes in the canonicalpathways related to HER-2 overexpression belonged tothis category,mainly in SKBR3 cells,whichdisplayed 60%to 80%of genes represented in citrate cycle,mitochondrialdysfunction, and oxidative phosphorylation as exclusive-ly regulated by the combination therapy.

Genes commonly regulated in HER2-overexpressingcell linesConsidering that our results showed the relevance of

HER-2 in the modulation of canonical pathways by thecombined treatment, we decided to determine a group ofgenes related to this response. Firstly, we compared thegene expression profiles generated from C52 and HB4a

cells, which showed that 27% (139 of 509) of the up-regulated and 65% (780 of 1,203) of the downregulatedgenes by the combined treatment were found only in C52compared with HB4a cells. Our next step was to identifywhich of these genes were also regulated in SKBR3,which resulted in a set containing 122 genes (16 up- and106 downregulated; Supplementary Table S4). Althoughmost of these genes have seemed not to exert knownroles in canonical pathways, theywere functionally anno-tated according to Gene Ontology database (Supplemen-tary Table S5).

Experimental validation by real-time RT PCRAmong the 122 genes corresponding to HER-2–medi-

ated response to the combined treatment, we selected 11genes to be validated by real-time RT-PCR (4 up- and 7downregulated; Supplementary Table S6). We observedvalidation of 6 genes (ALDOC, ELOVL6, PGHDH, TIPIN,

Figure 4. HB4a, C5.2, and SKBR3 cell lines were treated with varyingdosesof doxorubicin alone (30–1,000 nmol/L) and associatedwith a fixeddose of rapamycin (20 nmol/L) for 72 hours and growth inhibition wasdetermined by luminescence assays. Results are expressed aspercentage of the control. Experiments were conducted in 3 differentdays. Error bars represent the values of SEM. Dox, doxorubicin;Dox þ Rapa, combined treatment.

Figure 5. Venn diagrams of the number of differentially expressed genesgenerated from HB4a, C5.2, and SKBR3 cell lines after exposure todoxorubicin alone (D), rapamycin alone (R) and the combination of the 2drugs (D þ R).






WWC3, and ZNF323) in both C5.2 and SKBR3 cells,because their expression levels were significantly alteredin the same direction of microarray experiments (Supple-mentary Fig. S5). In addition, 2 genes were individuallyvalidated in C5.2 cells (BIK and DET1) and SKBR3 cells(AMD1 andCORO1A). ThegeneTXNIP showeda trend tobe validated in SKBR3 cells (P ¼ 0.0513). Overall, ourresults showed a validation rate of microarray resultsfrom 54% (6 of 11) considering both cell lines to 72% (8of 11) for each cell line individually.

Network constructionIt was recently shown that examination of human

interactome dynamics can evidence substantial altera-tions in its organization, besides quantitative gene expres-sion changes (26). Using the 122 aforementioned genes,we constructed a gene–gene interaction network based onthe human interactome to explain the molecular mechan-isms whereby HER-2 might mediate the response to thecombined treatment. After selecting the shortest path-ways among these genes, we calculated the PCC basedon normalized expression values in each cell line (HB4a,C5.2, and SKBR3). Subsequently, we compared the PCCvalues of both C5.2 and SKBR3 cells with those obtainedfor HB4a cells.

Among the 7,381 genepairs resulting from this analysis,we found that 38 showed differences larger than 1.5(Supplementary Table S7). Interestingly, these gene pairsallowed us to construct a network in which interactionswere inverted by HER-2 overexpression, because thesegenes were coexpressed in response to the combinedtreatment in HB4a cells (positive PCC) and became inde-pendently expressed in both HER-2–overexpressing celllines (negative PCC; Fig. 6). The TCF19 (SC1) was a hubgene displaying a highly correlated pattern of coexpres-sion with several genes and the gene C17orf95was exclu-sively regulated by the combined treatment. We alsoobserved that 4 genes represented in this network(PHGDH, TXNIP, WWC3, and CORO1A) were validatedby real-time RT-PCR.

Discussion

In this study, our purpose was to investigate whetherrapamycin could modulate the response of 2 cell linesharboring HER-2 overexpression or amplification todoxorubicin. Immortalized breast epithelial cells obtainedfrom normal mammary epithelium (HB4a) were alsoanalyzed.

Consistent with previous works, we observed that theeffects of doxorubicin on cell-cycle distribution consistedof increased arrest in S to G2–M phase (27) whereasrapamycin caused a G0–G1 arrest (28). Moreover, the 3cell lines displayeddifferences about the response to thesedrugs. Results of cell-cycle distribution showed thatHB4acells were themost sensitive to doxorubicin, whereas C5.2and SKBR3 cells were more sensitive to rapamycin. Thecombined treatment showed a preponderance of doxo-

rubicin-mediated S to G2–M arrest of HB4a and C5.2 cells,whereas SKBR3 cells displayed prominent effects of rapa-mycin over doxorubicin as shownbyG0–G1 arrest of thesecells.

According to previous works (29, 15), our cell growthassays showed that inhibition of mTOR caused a de-crease of doxorubicin IC50 for all cell lines. BecauseHB4acells lack HER-2 overexpression, we expected they weremore resistant to the effects of the combined treatmentcomparedwith theHER-2–overexpressing cell lines. Theability of rapamycin to increase the chemosensitivityof cell lines depends on constitutive activation of theAKT pathway (30). Indeed, our Western blotting resultsshowed that the HB4a cells, like C5.2 and SKBR3, hadconstitutive levels of phosphorylation ofAKTanddown-stream targets of mTOR (p70S6K and 4EBP1). We sup-pose that the high levels of EGFR receptor describedfor HB4a cells lead to the formation of EGFR–HER3heterodimers, which might trigger an alternative signal-ing cascade that activates AKT/mTOR pathway andincreases the response of HB4a cells to the growth inhi-bitory effects of the combined treatment (21).

OurWestern blotting analyses also showed a significantdecrease of p70S6K activation after rapamycin and itsassociationwith doxorubicin for all cell lineswhereasAKTremained activated irrespective of the treatment. Theseresults might be explained, in part, by the fact that doxo-rubicin has been shown to activate AKT pathway (31).In addition, mTOR leads to a feedback inhibition ofPI3K/AKT through activation of p70S6K so that its inhi-bition by rapamycin decreases p70S6K activity and, there-fore, abolishes this regulatory loop, resulting in AKTactivation (32).Decreasedphosphorylation levels of 4EBP1were observed only for SKBR3 cells undergoing rapamy-cin and combined treatment. These data do not necessarilyindicate resistance to treatment because p-4EBP1 levelsmight not correlate with rapamycin sensitivity (33).

Gene expression profiling was carried out to betterunderstand the processes involved in HER-2–mediatedresponse to the combined treatment. For this purpose, weidentified canonical pathways commonly regulated inC5.2 and SKBR3 and not evidenced in HB4a cells. Down-regulated genes were categorized in purine metabolism,cyclins and cell-cycle regulation, protein ubiquitination,lysine degradation, oxidative phosphorylation, mito-chondrial dysfunction, ubiquinone biosynthesis, and cit-rate cycle. Considering that our recent report showedenrichment of oxidative phosphorylation for genes upre-gulated by HER-2 overexpression (34), these data suggestthat thepathways identifiedbyour approachwere, in fact,specifically related to HER-2 overexpression.

Increasing evidence has shown that protein-basednetwork analysis might provide a more effective meansto identify molecular biomarkers involved in responseto treatments (35), because it allows to study gene–geneinteractions related to a set of biological processes insteadlooking at a specific pathway (Fig. 6). Using more strin-gent criteria to understand the effects of HER-2 on the

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response to the combined treatment, we selected only thegenes exclusively regulated inHER-2–overexpressing celllines for network construction. Among the 122 genesanalyzed, we found a subgroup of genes in which inter-connections in HB4a were inverted in C5.2 and SKBR3cells and, therefore, might represent a putative set ofmarkers for HER-2–mediated response to the combinedtreatment.Our network analyses showed the TCF19 (SC1) gene as

a central player (hub) of the interaction network, because

it presented disruption of interactions with several genesin response of HER-2–overexpressing cells to the com-bined treatment. These data are consistent with the roleof this gene as a relevant mediator of cell growth andtranscription of targets required for G1–S transition andentry to S to G2–M (36). Among the genes interactingwith TCF19, we found PHGDH, which encodes for3-phosphoglycerate dehydrogenase, an important medi-ator of production of biosynthetic precursors requiredfor cell proliferation. The diversion of glycolysis into

Figure 6. Network generated from genes related to HER-2–mediated response to the combined treatment and based on the human interactome. PCCcalculated among normalized expression values of the genes is represented by the lines connecting genes and colors vary according to these values:the red lines correspond to PCC values close to �1 and the green lines represent PCC values close to 1. Only gene pairs related to significantdifferences (>1.5) of PCC values in HB4a compared with C5.2 and SKBR3 cells were selected for network construction.






serine biosynthetic pathway by this enzyme leads toaccumulation of serine and a-ketoglutarate essential forthe synthesis of proteins, nucleotides, carbohydrates,and lipids that occurs through citrate cycle in mitochon-dria (37).

In addition to its role in these biosynthetic processes,mitochondria also supplies 40% to 75% of ATP require-ments in cancer cells, which indicates that therapeuticinterventions targeting this organelle might contribute tomitochondrial dysfunction and could lead to loss of cellviability. Indeed, other genes represented in the networkencode for mitochondrial components, such as mitochon-drial ribosomal protein (MRPS7) and translocases(TIMM50, TIMM10, and TOMM40L), which recognizeandmediate the import of nucleus-encodedmitochondri-al precursor proteins (38).

Altogether, these data suggest that the combinedtreatment might affect the mitochondria, which is sup-ported by previous reports unraveling the coordinatedrelationship between mTOR and mitochondrial metab-olism. Inhibition of mTOR by rapamycin has beendescribed to decrease mitochondrial membrane poten-tial, oxygen consumption, and ATP synthetic capacity(39, 40).

Other genes represented in the network involvedupstream regulators of AKT/mTOR (IMPA2, CORO1A,andTXNIP). TheCORO1A (41) and IMPA2 (42) genes havebeen described as involved in the phosphatidylinositolsignaling, which triggers a signaling cascade that activatesthe AKT protein. The gene TXNIP was related to thestimulation of PTEN protein, a negative regulator of thispathway, which indicates a relevant role of this gene in theattenuation of AKT/mTOR effects on cell growth (43).

Other genes in which interactions with TCF19 weredisrupted play functions related to DNA metabolism(ORC1L,GINS2, andDTYMK). TheDTYMK gene encodesfor thymidylate kinase, a key enzyme for dTTPs supply inDNA synthesis of cancer cells. Knockdown of GINS2 wasreported to lead to growth inhibition and polyploidy bythe suppression of M-phase progression in human breastcancer cells, showing that besides its roles in DNA rep-lication initiation, this gene might also facilitate cell divi-

sion and chromosome segregation required for cellgrowth (44).

We also noticed genes encoding for enzymes involvedin energy homeostase, such as CKB (creatine kinase)and FKBP11 (peptidyl prolylisomerase), with the latterdescribed as inhibited by rapamycin (45).

Conclusions

Our results suggest that HER-2–mediated response tothe combined treatment might lead to alterations of path-ways related to mitochondrial metabolism. A proteinnetwork analysis of a smaller set of genes correspondingto this response showed a loss of coordinated coexpres-sion of genes related to oxidative phosphorylation, citratecycle, mitochondrial proteins, and DNA metabolism,which might contribute to the impairment of processesrelevant for increased cell survival mediated by HER-2.We suppose that, compared with strategies based onindividual gene markers analyses, our network might bemore robust in predicting response ofHER-2–overexpres-sing breast carcinomas to the combined treatment withdoxorubicin and rapamycin.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

The authors thank Dr. Tiago Goss dos Santos for carrying out someassays, Dr. Walcy Paganelli R. Teodoro for the data interpretation,Dr. AnaMaria A. Camargo for providing SKBR3 cell lines, Mrs Maria Jos�eGoncalves Benevides for secretarial help, and Mrs. Cristina Pi~neiroGrandal for figure edition.

Grant Support

This work was supported by FAPESP and CnPq.The costs of publication of this article were defrayed in part by the

payment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received January 26, 2011; revised October 10, 2011; accepted October26, 2011; published OnlineFirst November 14, 2011.

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Published OnlineFirst November 14, 2011.Mol Cancer Ther Adriana Priscila Trapé, Maria Lucia Hirata Katayama, Rosimeire Aparecida Roela, et al. Human Mammary Epithelial Cell Lines

Overexpressing−Rapamycin Combined Treatment of HER-2−Gene Expression Profile in Response to Doxorubicin

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