identiﬁcation of docetaxel resistance genes in castration … · therapeutic discovery...

Therapeutic Discovery

Identification of Docetaxel Resistance Genes inCastration-Resistant Prostate Cancer

Mercedes Marín-Aguilera1, Jordi Codony-Servat1, Susana G. Kalko2, Pedro L. Fern�andez3,Raquel Bermudo5, Elvira Buxo1, María Jos�e Ribal4, Pedro Gasc�on1, and Bego~na Mellado1

AbstractDocetaxel-based chemotherapy is the standard first-line therapy in metastatic castration-resistant prostate

cancer (CRPC).However,most patients eventuallydevelop resistance to this treatment. In this study,weaimed

to identify keymolecular genes and networks associatedwith docetaxel resistance in twomodels of docetaxel-

resistant CRPC cell lines and to test for themost differentially expressed genes in tumor samples from patients

with CRPC. DU-145 and PC-3 cells were converted to docetaxel-resistant cells, DU-145R and PC-3R,

respectively. Whole-genome arrays were used to compare global gene expression between these four cell

lines. Results showeddifferential expression of 243 genes (P< 0.05, Bonferroni-adjustedPvalues and log ratio >1.2) that were common to DU-145R and PC-3R cells. These genes were involved in cell processes like growth,

development, death, proliferation, movement, and gene expression. Genes and networks commonly deregu-

lated in both DU-145R and PC-3R cells were studied by Ingenuity Pathways Analysis. Exposing parental cells

to TGFB1 increased their survival in the presence of docetaxel, suggesting a role of the TGF-b superfamily in

conferring drug resistance. Changes in expression of 18 selected geneswere validated by real-time quantitative

reverse transcriptasePCR in all four cell lines and tested in a set of 11FFPEandfiveoptimal cutting temperature

tumor samples. Analysis in patients showed a noteworthy downexpression ofCDH1 and IFIH1, among others,

in docetaxel-resistant tumors. This exploratory analysis provides information about potential gene and

network involvement in docetaxel resistance in CRPC. Further clinical validation of these results is needed

to develop targeted therapies in patients with CRPC that can circumvent such resistance to treatment.

Mol Cancer Ther; 11(2); 329–39. �2011 AACR.

Introduction

Prostate cancer is the second most common canceramong men worldwide (1). Approximately 85% of newlydiagnosed prostate cancer cases are localized to the pros-tate, whereas the remainder are invasive or metastaticdisease (2). Patients with metastatic prostate cancerrespond initially to antiandrogen therapy. However,tumors eventually progress and transform themselvesinto castration-resistant prostate cancer (CRPC). Doce-taxel improves survival of patients with metastatic CRPCand is considered a standard first-line therapy in suchcases (3; 4). However, only approximately 50% of patients

respond to docetaxel and most of them eventually devel-op resistance to this therapy.

Taxanes bind b-tubulin, stabilizing microtubulesassembly and preventing depolymerization in theabsence of GTP (5). Furthermore, docetaxel leads to Bcl-2 phosphorylation, which causes apoptosis of cancer cellsthat had previously blocked the apoptotic-inducingmechanism, leading to tumor regression (6). Some pro-teins such as stathmin, Aurora-A, and b-III tubulin havebeen described to be involved in resistance to antimicro-tubule agents (7–9). However, mechanisms of resistanceto docetaxel in CRPC need to be further elucidated todesign targeted therapies that can circumvent treatmentresistance.

cDNA microarrays technology has given us the abilityto simultaneously examine the expression of thousands ofgenes and determine the molecular profile of clinicalphenotypes, some of which can be involved in specificcell profiles, such as chemotherapy resistance. This studycompared gene expression profiles of 2 models of doc-etaxel-resistant CRPC cell lines, and identified a set ofgenes involved in resistance to this chemotherapy. Themost differentially expressed genes were validated byreal-time quantitative reverse transcriptase PCR (qRT-PCR) in cell lines and, in an exploratory way, in CRPCtumor samples.

Authors' Affiliations: 1Laboratory and Medical Oncology Department,2Bioinformatics Support Unit, Departments of 3Pathology and 4Urology,Hospital Clínic; and 5Human and Experimental Functional Oncomorphol-ogy Department, Institut d'Investigacions Biom�ediques August Pi i Sunyer,Barcelona, Spain

Note: Supplementary data for this article are available at Molecular CancerTherapeutics Online (http://mct.aacrjournals.org/).

Corresponding Author: Bego~na Mellado, Medical Oncology Department,Hospital Clínic de Barcelona, Villarroel 170, Barcelona 08036, Spain.Phone: 34-93-227-5400, ext. 2262; Fax: 34-93-454-6520; E-mail:[email protected]

doi: 10.1158/1535-7163.MCT-11-0289

�2011 American Association for Cancer Research.

MolecularCancer

Therapeutics

www.aacrjournals.org 329

on January 25, 2020. © 2012 American Association for Cancer Research. mct.aacrjournals.org Downloaded from

Published OnlineFirst October 25, 2011; DOI: 10.1158/1535-7163.MCT-11-0289

http://mct.aacrjournals.org/

Materials and Methods

Cell cultureThe human prostate carcinoma cell lines DU-145 and

PC-3 (obtained from American Tissue Culture Collec-tion) were converted to docetaxel-resistant cells byexposing them to an initial dose of 5 nmol/L doce-taxel and culturing surviving cells during 1 year and6 months, respectively, with increasing doses in anintermittent regimen. DU-145 and DU-145R cell lineswere cultured in RPMI-1640 medium (Invitrogen), andPC-3 and PC-3R in F-12K nutrient mixture medium(Invitrogen), both supplemented with 10% FBS. Nofurther authentication of the cell lines was done by theauthors. Docetaxel (Sigma) was dissolved in dimethylsulfoxide (10 mmol/L). IC50 values were estimated intriplicate from the dose–response curves.

Microarray hybridizations and differentialexpression analysis

Total RNAs were isolated from DU-145, DU-145R,PC-3, and PC-3R cell lines (in triplicate) using the TRIzolReagent (Invitrogen) according to the manufacturer’sinstructions. RNAs were purified using RNeasy Microkit (Qiagen), and quality and quantity were assessed ona spectrophotometer. Fragmented, labeled, and ampli-fied cDNA was hybridized to the Affymetrix HumanGenome U133Plus2.0 array, which represents about38,500 well-characterized genes. Washes and scanningof the arrays were carried out according to manufac-turer’s instructions.

Raw expression measures were summarized afterbackground correction and normalization steps usingthe robust multiarray analysis (RMA) methodology inthe affy (10) package from the Bioconductor project (11).Unsupervised cluster analysis of high variability geneswas done with dChip v1.3 software (12). Differentialexpression analysis was carried out by a linear modelusing the empirical Bayes method to moderate thestandard errors of the estimated log ratio changes withthe limma package (13), according to adjusted P valuesless than 0.05 for the comparison of interest. Specialattention was given to the statistically significant genes

that showed the largest changes ( logratio��

�� > 1:2). CEL

files and RMA values were deposited on Gene Expres-sion Omnibus (GSE33455).

Network analysisGene interactions were studied using the Ingenuity

Pathway Analysis (IPA; Ingenuity Systems) software(14), according to IPA instructions. First, genes differen-tially expressed between resistant and parent cell lines(DU-145R vs. DU-145 and PC-3R vs. PC-3) were listed.Interactions between common deregulated genes in DU-145R and PC-3R cells were then analyzed to understandresistance mechanisms at a molecular level. Network-eligible genes were placed by IPA into networks andrankedby the relevanceof thenetwork-eligiblemolecules.

Patients and samplesTumor samples from patients with metastatic prostate

cancerwere collected to testmicroarray results inpatients.We selected patients who were prescribed docetaxel-based therapy after the palliative transurethral deso-bstructive resection, or after being biopsied once thetumor had already spread. Samples were obtained beforethe start of docetaxel treatment.

We were able to collect 11 formalin-fixed, paraffin-embedded (FFPE) samples, and 5 tumor samples embed-ded in 1 to 2 mL of optimal cutting temperature (OCT)medium immediately after surgery. The OCT sampleswere stored at �80�C until processing. The institutionalreview board approved this study and written informedconsent was obtained from all patients.

FFPE samples belonged to 11 patients with CRPC, all ofwhom had bone metastasis; 5 also had lymph nodelesions, 2 had lung metastasis, and 2 others had liver andureteral tumor growth. Six patients partially responded todocetaxel and the other 5 progressed during the treat-ment. On the other hand, OCT samples were extractedfrom5patientswithCRPCwith bonemetastasis, ofwhom4 also had a lymph node lesion and 1 had liver metastasis.Two patients partially responded to docetaxel and theother progressed during the treatment.

Treatment response was evaluated by prostate-specificantigen–based response criteria (15) and Response Eval-uation Criteria in Solid Tumors (RECIST; ref. 16).

Gene validation in cell linesGenes that could potentially be related with docetaxel

resistance were selected for further validation in cell linesusing qRT-PCR. Genes were selected according to their

change in degree of relative expression ( logratio��

�� > 1:2),

but also by function, that is, genes involved in knownpath-ways such as cell adhesion, cell signaling, or regulationof apoptosis, using DAVID (17; 18) and IPA softwares (14).

Total RNAs fromcell lineswere isolatedusing theTRIzolReagent (Invitrogen) according to the manufacturer’sinstructions. One microgram of total RNA was reversetranscribed using the High Capacity cDNA Archive Kit(Applied Biosystems), following manufacturer’s instruc-tions. qRT-PCR was carried out in a 7500 Real Time PCRsystem (Applied Biosystems), according to the manufac-turer’s recommendations. Data were acquired using SDSSoftware 1.4. Expression values were based on the quan-tification cycle (Cq) from target genes relative to ACTBendogenous control gene (DCq). Relative expression withrespect to each reference group studied was reported aslog ratio. Commercial codes for primers and probes fromtarget genes were AREG (amphiregulin-Hs00950669_m1),CDH1 (E-cadherin-Hs01023895_m1), CYBRD1 (cyto-chrome b-reductase 1-Hs00227411_m1), DLC1 (deletedin liver cancer 1-Hs00183436_m1), GJB2 (gap junctionprotein, b 2-Hs00955889_m1), GSPT2 (G1 to S-phase tran-sition 2-Hs00250696_s1), IFIH1 (IFN induced with heli-case C domain 1-Hs01070332_m1), IL8 (interleukin8-Hs00174103_m1), MAPK13 (mitogen-activated protein

Marín-Aguilera et al.

Mol Cancer Ther; 11(2) February 2012 Molecular Cancer Therapeutics330




kinase 13-Hs00559623_m1), MPZL2 (myelin protein zero-like 2-Hs00170684_m1), MX1 (myxovirus resistance1-Hs00895608_m1), MYO6 (myosin VI-Hs00192265_m1), S100A4 (S100 calcium–binding protein A4-Hs00243202_m1), SERPINA1 (serpin peptidase inhibitor,clade A-Hs01097800_m1), SYK (spleen tyrosine kinase-Hs00895377_m1), EPCAM (epithelial cell adhesionmolecule-Hs00901885_m1), NEAT1 (nuclear paraspeckleassembly transcript 1-Hs01008264_s1), and TNFAIP3 (tu-mor necrosis factor, a-induced protein 3-Hs00234713_m1).

Gene expression in tumor samplesTissues from selected FFPE and OCT blocks were sec-

tioned at 10 and 20 mm thicknesses, respectively, imme-diately before the RNA extraction. Total RNA from FFPEsamples was obtained using the RecoverAll Total NucleicAcid Isolation Kit for FFPE (Ambion). The RNAs fromOCT samples were extracted using TRIzol Reagent (Invi-trogen) according to the manufacturer’s instructions.Quality and quantity of total RNAs were measured byND-1000 Spectrophotometer (Nanodrop Technologies).The genes selected for microarrays validation in cell

lines were also tested in tumor samples following thesame qRT-PCR protocol except that the expression valuesof target geneswere relative to theCqmeanofACTB,B2M,and GUSB endogenous genes.

Viability assayTGFB1 (Sigma-Aldrich) was dissolved in sterile 4

mmol/L HCl containing 0.1% endotoxin-free recombi-nant human serum albumin (50 mg/mL). Parent cells(DU-145 and PC-3)weremaintained during 7 consecutivedayswith TGFB1 (5 ng/mL) in themedium. Parental cellswith andwithout sustained treatmentwere then seeded atadensity of 3,200 cells perwell in a 96-wellmicrotiter platein the corresponding medium with 10% FBS. After 24hours, cells were exposed to docetaxel with/withoutTGFB1 for an additional 72 hours. Finally, cell prolifera-tion/viability was assessed by using MTT colorimetricassay (Promega).

Statistical analysisChanges in gene expression comparing docetaxel-

resistant and docetaxel-sensitive cell lines and tumorsamples by qRT-PCR were analyzed with Wilcoxonrank-sum test, considering as positively validated thosegenes with significant expression changes (P < 0.05).SPSS 12.0 software was used for statistical analyses.

Results

Docetaxel-resistant cell linesDU-145R and PC-3R cells acquired levels of resistance

to docetaxel that was 2 to 5 times higher than their parentcells. IC50 values for DU-145R and PC-3R ranged from 10to 15 and 20 to 22 nmol/L, respectively, whereas DU-145and PC-3 registered from 4 to 5 and 3 to 5 nmol/L,respectively (data not shown).

Differentially expressed genes in resistant cellsInitially, unsupervised clustering analysis of microar-

rays was carried out using the probe sets that varied mostthroughout the whole experiment, and this explorationrevealed a good segregation of the arrays in their respec-tive classes on the basis of expression values (data notshown). Subsequent differential expression analysisrevealed 1,064 and 1,361 differentially expressed genes(Bonferroni < 0.05 and log ratio > 1.2), equivalent to 1,710and 2,117Affymetrix probe sets, betweenDU-145RversusDU-145 and PC-3R versus PC-3, respectively (Supple-mentaryTable SI). Top20over- anddownexpressedgenesfrom docetaxel-resistant cells with respect to their parentcells according to log ratio expression values, and theirmain function, are summarized in Supplementary TableSII. When comparing differentially expressed genesbetween both resistant cell lines (DU-145R and PC-3R),243 genes overlapped (Supplementary Table SIII). Fromthose genes, 172 were over- or downexpressed in bothDU-145R andPC-3R cells versus their parent cells. The top10 commonly over- and downexpressed genes, with theirrespective main function, are summarized in Table 1.

Ingenuity network analysisOnly considering probe sets common to both resistant

cell lines, IPAqualified 252 genes as network and functioneligible. A core analysis showed 12 networks on the basisof this gene selection with a score more than 2. The 2 topnetworks were detected with scores of 51 and 39 respec-tively, and were the same for DU-145R and PC-3R celllines (Fig. 1). Specifically, network1 (Fig. 1A)was centeredon, amongst others, the nuclear receptor PPARA (perox-isome proliferator-activated receptor a). This gene direct-ly interacts with an important complex for cell survivaland proliferation, NFKB, which in turn interacts withTNFAIP3,HMGA1 (high mobility group AT-hook 1), andISG15 (ISG15 ubiquitin-like modifier). Other deregulatedgenes directly related toPPARA are SOCS2 (suppressor ofcytokine signaling 2), ASL (argininosuccinate lyase),ASNS (asparagine synthetase), ELOVL6 (ELOVL familymember 6), IGFBP6 (insulin-like growth factor bindingprotein 6) and CITED2 (Cbp/p300-interacting transacti-vator, with Glu/Asp-rich carboxy-terminal domain 2).Other genes commonly deregulated in network 1 areSERPINA1 (serpin peptidase inhibitor, clade A), VDR(vitamin D receptor), ITGB2 (integrin, b 2), and TGFBR3(TGF-b receptor III, also known as betaglycan; Fig. 1A).The last is overexpressed in both DU-145R and PC-3Rcells. Interestingly, other TGF-bmembers appear deregu-lated in the same network, such as TGFB2, which isdifferentially regulated in both cell lines, and TGF-bligand, which is included by the software as a linkbetween the former TGF-b elements and LTBP2 (latentTGF-b binding protein 2).

Network 2 (Fig. 1B) is mainly centered on the CDH1gene, which codifies a classical calcium dependent cell–cell adhesion glycoprotein. MYO6 (myosin VI), ID2(inhibitor of DNA binding 2), PTPRM (protein tyrosine

Docetaxel Resistance in Castoration-Resistant Prostate Cancer

www.aacrjournals.org Mol Cancer Ther; 11(2) February 2012 331




Tab

le1.

Top10

common

lyov

er-a

nddow

nexp

ressed

gene

sinDU-145

Ran

dPC-3Rve

rsus

theirp

aren

tcells,w

iththeirres

pec

tivemainfunc

tion

andlogratio

expression

Ove

rexp

ress

ed

DU-145

Rvs

.DU-145

PC-3R

vs.P

C-3

Gen

esy

mbol

Logratio

Mainfunc

tion

Gen

esy

mbol

Logratio

Mainfunc

tion

GSPT2

4.48

7GTP

ase

NEAT1

3.46

8Non

–protein

coding

ID2

4.05

0Multic

ellularorga

nism

dev

elop

men

t;regu

latio

nof

tran

scrip

tion

FLJ2

7352

3.28

7Und

efine

d

CXCR7

3.87

9Signa

ltrans

duc

tion

TGFB

R3

2.96

0Epith

elialtomes

ench

ymal

tran

sitio

n;sign

altran

sduc

tion;

cellcy

cle;

cellmigratio

nNDRG1

3.23

3Stres

san

dho

rmon

eresp

onse

s,ce

llgrow

than

ddifferen

tiatio

nPLS

CR4

2.55

2Pho

spho

lipid

scrambling

FRMD3

3.19

1Struc

turalp

rotein

HTR

A1

2.38

9Reg

ulationof

cellgrow

thTP

53INP1

2.79

7Cell-cy

clearrest;ind

uctio

nof

apop

tosis

S10

0A4

2.21

7Epith

elialtomes

ench

ymal

tran

sitio

n;ce

ll-cy

cleprog

ressionan

ddifferen

tiatio

n.GOSR2

2.43

4Intrac

ellularprotein

tran

sport

CYBRD1

2.15

8Tran

sport;ox

idationredu

ction

CITED2

2.37

2Vas

culoge

nesis;

resp

onse

tohy

pox

ia;

pos

itive

regu

latio

nof

cell–ce

llad

hesion

;pos

itive

regu

latio

nof

cellcy

cle

PPP2R

2C2.05

2Signa

ltrans

duc

tion

KLH

L24

2.30

9Und

efine

dGSPT2

2.00

6GTP

ase

NEAT1

2.26

7Non

–protein

coding

PC

1.97

9Metab

olism,ins

ulin

secretion,

andsynthe

sis

ofthene

urotrans

mitter

glutam

ate

Downe

xpress

edSCEL

–6.42

8Epidermis

dev

elop

men

t;as

sembly

orregu

latio

nof

structural

proteins

ESRP1

–6.61

2Reg

ulationof

RNAsp

licing

EPCAM

–5.35

7Calcium

-ind

epen

dent

cellad

hesion

TACSTD

2–6.30

9Signa

ltrans

duc

tion

C1o

rf11

6–5.29

2Und

efine

dEFE

MP1

–6.07

9Extrace

llularmatrix

glyc

oprotein

TACSTD

2–5.24

0Signa

ltrans

duc

tion

GJB

2–5.98

4Tran

sport;ce

ll–ce

llsign

aling

SERPINA1

–5.15

3Res

pon

seto

hypox

iaMPZL2

–5.08

8Cella

dhe

sion

AIM

1–4.87

6Und

efine

dRBM47

–4.33

3Und

efine

dAREG

–4.86

4EGFrece

ptorsign

alingpathw

ay;

cellprolife

ratio

n;ce

ll–ce

llsign

aling

JPH1

–4.31

2Calcium

iontran

sportinto

cytoso

l

JPH1

–3.99

2Calcium

iontran

sportinto

cytoso

lTX

NIP

–4.22

4Tran

scrip

tion;

resp

onse

toox

idative

stress;reg

ulationof

cellprolife

ratio

nGPR87

–3.64

8Signa

ltrans

duc

tion

GPR87

–4.19

2Signa

ltrans

duc

tion

GALN

T3–3.50

1Carboh

ydrate

metab

olic

proce

ssKAP2.1B

–4.18

1Keratin-ass

ociatedprotein






A

B

Figure 1. Gene networks deregulated in resistant cell lines with respect to their parent cells. A, the most significant (P < 0.05) gene network deregulated inresistant cells, scored 51 by IPA software. B, the second most significant (P < 0.05) gene network deregulated in resistant cells, scored 39 by IPA software.






phosphatase, receptor type M), and OCLN (occludin)are other deregulated genes directly related withCDH1.

No overlapping genes were found between networks 1and 2, although both are involved in gene expression.

Functional in silico analysisIPA detected several biologic functions with overrep-

resentation, when considering together the differentiallyexpressed genes in docetaxel-resistant versus their parentcells. Such functions were related with elemental metab-olism of cell survival like gene expression, cell growth andproliferation, cell cycle, cell death, and cell movement.Specifically, and as expected, the 2 main networks wereinvolved in cell growth and proliferation, but also ingene expression, cell movement and development, andthe skeletal and muscular system.

The top overexpressed individual geneswere enzymes,cell surface receptors, transcription regulators, and struc-tural proteins involved in cell transport, signaling, bind-ing, and cytoskeletal organization. Similarly, proteinsfrom downexpressed genes were structural proteins, sig-nal transducers, enzymes, growth factors, cell receptors,and transcription factors involved in cell structure, adhe-sion, transport, and cycle regulation.

Cell viability assay for TGF-b familyThe TGF-b superfamily was represented at network 1

and was overexpressed in resistant cell lines. We selectedthis target for a functional study of docetaxel resistance.The results of MTT experiments showed that continuoustreatment with TGFB1 (5 ng/mL) increased DU-145 andPC-3 cell viability until 16.5% and 15.6%, respectively,with respect to cells thatwerenot culturedwith the ligand.Furthermore, when parental cells were exposed to TGFB1

(10 ng/mL) for 72 hours, cell proliferation was until 27%greater in PC-3 cells at 7.5 nmol/L of docetaxel than incells without TGFB1 treatment. Cell viability of DU-145cells was not affected by TGFB1 under these experimentalconditions (Fig. 2).

Validation by qRT-PCR in cell lines and test in tumorsamples

A panel of 18 of the top 20 with the highest or lowestexpression range in both DU-145R and PC-3R versusparent cells lines (seematerials andmethods)was selectedfor being commonly over- or downexpressed.

It was possible to confirm by qRT-PCR significant dif-ferences in expression in all 18 (Wilcoxon test, P < 0.05)in the 4 cell lines studied (Fig. 3). Moreover, their expres-sion was analyzed in docetaxel-resistant (n ¼ 5) versusdocetaxel-sensitive (n ¼ 6) FFPE tumor samples, anddocetaxel-resistant (n ¼ 3) versus docetaxel-sensitive(n¼ 2) OCT tumor samples from patients with metastaticCRPC. This analysis showed a significant downexpres-sion of NEAT1 (P ¼ 0.044) in FFPE tumors. Of the 18markers studied, 10 markers in the FFPE samples and 13in OCT samples were deregulated in the same way as inthe in vitro models. Interestingly, as shown in Fig. 4, 7 ofthese marker genes were commonly downexpressed indocetaxel-resistant cell lines and in FFPE and OCT sam-ples from patients with CRPC resistant to docetaxel:AREG, CDH1, DLC1, GJB2, IFIH1, MX1, and EPCAM(Fig. 4).

Discussion

This study revealed potential genes and networksinvolved in resistance to docetaxel in 2 CRPC cell lines,DU-145 and PC-3. Both cell lines were converted to

A

B

DU-145 (sustained TGFB1)

DU-145 (TGFB1, 72 h) PC-3 (TGFB1, 72 h)

PC-3 (sustained TGFB1)

120

100

80

60

40

20

0

Docetaxel

Docetaxel + TGFB1

Docetaxel

Docetaxel + TGFB1

Docetaxel

Docetaxel + TGFB1

Docetaxel

Docetaxel + TGFB1

Ctrl. 0 1 1.8 2.5

% c

ell

via

bili

ty

120

100

80

60

40

20

0

% c

ell

via

bili

ty

120

100

80

60

40

20

0

% c

ell

via

bili

ty

120

100

80

60

40

20

0

% c

ell

via

bili

ty

5 7.5 10 20 Docetaxel (nmol/L)

Ctrl. 0 1 1.8 2.5 5 7.5 10 20 Docetaxel (nmol/L)



TGFB1 (5 ng/mL) TGFB1 (5 ng/mL)

TGFB1 (10 ng/mL) TGFB1 (10 ng/mL)

Figure 2. Cell viability assay ofdocetaxel treatment for 72 hours inDU-145 and PC-3 cells. A, after asustained treatment with TGFB15 ng/mL during 7 days plusadditional 72 hours and after72 hours of treatment with TGFB110 ng/mL (B). Cell viability isexpressed as the relativecolorimetric signal calculated fromthe untreated wells (Ctrl.). Eachexperimental condition was carriedout in triplicate (SD is shown).






docetaxel-resistant and then microarrays analysis of par-ent and resistant cells was carried out.Docetaxel resistance was achieved with nanomolar

concentrations of the drug (10–15 and 20–22 nmol/L forDU-145R andPC-3R, respectively). The fact that docetaxelIC50 in resistant cells was not substantially higher than insensitive cells is consistent with other in vitro docetaxelresistance studies (19; 20). This issue may be partiallyexplained by the fact thatmicrotubule alterations inducedby taxanes can occur even at concentration of 1 nmol/L(21).Microarrays analysis was focused on the identification

of commonly deregulated genes in the 2 different cell linemodels. We found 243 eligible genes (Bonferroni < 0.05and log ratio > 1.2) that were similarly deregulated in DU-145R and PC-3R with respect to their parent cell lines.These genes were involved in survival functions such asgene expression and cell growth, proliferation, death, andmovement. Interestingly, in both cell lines GSPT2 andNEAT1 were within the top 10 overexpressed genes, andTACSTD2 (tumor-associated calcium signal transducer2), JPH1 (junctophilin 1), and GPR87 (G-protein–coupled

receptor 87) within the top 10 downexpressed genes.Among these genes, only TACSTD2 and GPR87 havealready been related with cancer in the literature (22; 23).

Briefly, GSPT2 encodes a GTPase that may be involvedin mRNA stability and NEAT1 is a non–protein-codingRNA that seems to regulate mRNA export. Such involve-ment of docetaxelwithmRNAregulation is not surprisingbecause, as previously described, taxanes can promotetranscription and mRNAs stabilization of some genes.Such stabilizing effects can be achieved by the stimulationof proteins that bind the AU-rich region of the 30UTRregion of target genes (24).

On the other hand, TACSTD2 encodes a carcinoma-associated antigen that is a cell surface receptor thattransduces calcium signals. It has been described to beunmethylated in normal prostate cells and prostaticintraepithelial neoplasia but hypermethylated in primaryprostate tumors (25), suggesting that methylation couldbe one of the mechanisms for silencing the expression ofcrucial genes, thus inactivating the apoptotic pathway inCRCP. The real significance of TACSTD2 intraexpressionin resistant cell lines and the potential role of docetaxel inmethylation-based regulation need to be further explored.

0.5

0

–0.5

–1

–1.5

–2

Genes

AR

EG

CD

H1

CY

BR

D1

DLC

1

GJB

2

GS

PT

2

IFIH

1

IL8

MA

PK

13

MP

ZL2

MX

1

MY

O6

S10

0A4

SE

RP

INA

1

SY

K

EP

CA

M

NE

AT1

TN

FAIP

3

Genes

AR

EG

CD

H1

CY

BR

D1

DLC

1

GJB

2

GS

PT

2

IFIH

1

IL8

MA

PK

13

MP

ZL2

MX

1

MY

O6

S10

0A4

SE

RP

INA

1

SY

K

EP

CA

M

NE

AT1

TN

FAIP

3

FFPE tumor samples

OCT tumor samples

Log r

atio

1.5

1

0.5

0

–0.5

–1

–1.5

–2

–2.5

–3

–3.5

Log r

atio

Figure 4. Differential gene expression by qRT-PCR in docetaxel-resistantversus docetaxel-sensitive FFPE and OCT tumor samples from patientswith metastatic CRPC. Expression data are represented by a log ratiocalculated comparing DCq from docetaxel-resistant patients with themedian of DCq from docetaxel-sensitive patients. DCq was calculated asthe difference between Cq of target genes and the mean of Cq of theendogenous control genes ACTB, B2M, and B-GUS. �, P < 0.05.

6

4

2

0

–2

–4

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AR

EG

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AR

EG

CD

H1

CY

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Microarray s

qRT-P CR

Microarray s

qRT-P CR

Figure 3. Validation of microarrays data by qRT-PCR in DU-145,DU-145R, PC-3, and PC-3R cell lines. Expression data are representedby a log ratio calculated by comparing DCq from resistant cells withDCq from parent cells. DCq was calculated as the difference between Cq

of target genes and Cq of the endogenous control gene ACTB.






Another downexpressed gene was JPH1, which med-iates cross-talk between the cell surface and intracellularion channels, modulating electrochemical gradients thatare vital to the cell and a potentially strong influence ondrug activity, according to previous work (26). Accordingto the authors, the expression of several genes that encodesubunits of sodium, chloride, potassium, and other cationchannels also is correlated with drug activity.

GPR87 encodes a G-protein–coupled receptor thatplays an essential role in many physiologic processes,including neurotransmission, immunity, and inflamma-tion. This gene seems to be overexpressed in diversecarcinomas and plays an essential role in tumor cellsurvival. A recent study revealed that a lack of GPR87triggers an increase in p53, concomitantwith a decrease inAKT, which results in the sensitization of tumor cells toDNA damage–induced apoptosis and growth suppres-sion (23). However, these results are in disagreementwiththe fact that resistant cells downregulate the expression ofGPR87 but remain able to survive and proliferate. Therole of several other genes that contribute to docetaxelresistance must account for such discrepancy.

Interestingly, several well-known molecules alreadyrelated with docetaxel resistance have been found to bederegulated in the present work. ABCA13, ABCA8, andABCC2, all from theABC (ATP-binding cassette) family ofdrug transporters, are significantly deregulated in DU-145R cells; onlyABCC2was already known to be involvedinmultidrug resistance (27). None of these genes has beenfound to be deregulated in both resistant cell lines. On theother hand, there are growing evidences about the role ofb-tubulin isotypes in resistance to taxanes in CRPC, asthey are the primary target of these drugs (9; 28). In thepresent study we observed that TUBB2B (tubulin, b 2B)was downexpressed in PC-3R cells versus their parent cellline (see Supplementary Table SI), but no differenceswereobserved in the expression of b-tubulins in the DU-145model. Furthermore, tubulin is one of the molecules in-cluded by the Ingenuity software as a link between EML1and MAP2, actin and calmodulin proteins (Fig. 1B);however, its expressionwasnot significantly deregulated.These results suggest that alternative pathways may bemore important in generating docetaxel resistance thanthose directly related to tubulin alterations.

Apart from an individual view of deregulated genes, inthis study we focused on IPA analysis that allowed us toobtain global and integratedmolecular information aboutinteractions between significant differentially expressedgenes. Results from such analysis showed that the mostsignificant network was centered on PPARA, which wasoverexpressed in docetaxel-resistant cells (Fig. 1A).PPARA is a nuclear receptor that regulates the expressionof multiple genes involved in cell proliferation, differen-tiation, and immune and inflammation responses. Thealpha form of this gene is functional in human prostateand is downregulated by androgens. It has been sug-gested that its overexpression in advancedprostate cancerindicates a role in tumor progression, with the potential

involvement of dietary factors (29). As shown in Fig. 1A,PPARA interacts with CITED2 (Cbp/p300-interactingtransactivator, with Glu/Asp-rich carboxy-terminaldomain 2),which has been involved in cisplatin resistance(30), and notably with the transcription regulators ETS1(v-ets erythroblastosis virus E26 oncogene homolog 1).Inappropriate expression of ETS1 has been observed in avariety of human cancers and might play an importantrole in carcinogenesis and/or the progression of humanprostate cancer (31). However, no link between ETS1expression and docetaxel resistance has been describedto date.

ETS1 directly interacts with VDR and ITGB2, whichalso are downexpressed. VDR is a transacting transcrip-tional regulatory factor involved in a variety of meta-bolic pathways, but also in antineoplastic activities. Infact, calcitriol, the most active metabolite of vitamin D,showed to enhance antitumor activity of docetaxel,although an effect on patients survival has not beenshown (32; 33). Downregulation of ITGB2 has beenpreviously associated with transition between prostaticintraepithelial neoplasia and prostate cancer, playing arole in cell adhesion of invasive prostate cancer cells(34).

A second focus of interest in network 1 is NFKB. Thisprotein complex has been widely associated with onco-genesis due to its ability to regulate cell proliferation andprotect cells from apoptosis (35). CRPC cell lines like DU-145 and PC-3 exhibit constitutive activation of NFKB,whereas its activity is low in the androgen-sensitiveLNCaP and LAPC-4 cells, which is consistent with therole ofNFKB in progression of prostate cancer. Moreover,higher levels of NFkB protein can be further enhanced inresponse to certain types of chemotherapy (36; 37). Ourgrouppreviously found that the inhibition of this complexmay be an attractive strategy to enhance docetaxelresponse in PC (38). This strategy has also been clinicallytested through the use of bortezomib (39; 40).

The present study specifically explored the role of theTGF-b superfamily, which is also represented at network1. The signaling pathway derived from this family ofproteins has a principal role in growth control (41). Asa member of this family, TGFBR3 acts as a coreceptor ofTGF-b ligand, being a positive or negative regulator ofTGF-b signals depending on the cellular context (42; 43).Recently, it has been shown that TGFBR3 may act as aprotective factor in the apoptotic process of fibroblasts bynegative regulation of TGF-b signaling (44). Our resultsfurther suggest that TGFB1 acts as a protective factoragainst docetaxel for DU-145 and PC-3 cells and has apartial role in the development of docetaxel resistance incultured cells. The specific mechanism through whichTGF-b/TGFBR3 protects DU-145 and PC-3 cells from thisdrug remains to be elucidated.

The epithelial cell adhesion molecule CDH1, which issignificantly downexpressed in docetaxel-resistant cells,is included in the second most significant networkobtained in this analysis (Fig. 1B). The loss of CDH1 in






metastatic cells has been shown in a variety of in vitro andin vivo models, and has been related to the epithelial-mesenchymal transition process (45–47). However, itsrole in chemotherapy resistance should be further stud-ied. The mere loss of CDH1 probably does not directlyconfer chemoresistance properties to the tumor cell, butsignals may be conveyed that induce resistance to che-motherapy. According to network 2, CDH1 directly inter-acts with the transcription regulator ID2 (inhibitor ofDNA binding 2), which is significantly overexpressedin resistant cells, and enzymes like OCLN, which is alsoinvolved in cell–cell adhesion and is downexpressed inresistant cells. Other CDH1-related genes in this networkare the phosphatase PTPRM, the plakophilin PKP2, andthe myosin MYO6.Another interesting focal gene downexpressed in net-

work 2 is IFI16 (interferon, gamma-inducible protein 16).The encoded protein contains domains involved in DNAbinding, transcriptional regulation, and protein–proteininteractions. It is known that this protein modulates P53function and inhibits cell growth in the RAS/RAF signal-ing pathway, an antitumor activity. Downexpression ofthis gene in resistant cells seems to be related to theirability to grow despite the presence of docetaxel in themedium.A recent study used microarray analysis to compare

the PC-3R cell line with andwithout docetaxel in culturemedia, to identify genes responsible for the multinucle-ated process that PC-3 cells suffer as a result of doc-etaxel exposure. The authors also compared DU-145with docetaxel-resistant DU-145 cells and showed a setof 10 genes present in both PC-3R and DU-145R cells(19). However, in contrast to our study only LAMC2,which increases more than 10% during hormone escapein prostate cancer (48), was represented in both models.Methodologic factors and differences in bioinformaticsanalysis between these studies could account for suchhigh variability of commonly deregulated genes indocetaxel-resistant cells.Eighteen genes were selected according to their func-

tion and degree of relative expression in resistant cellsversus parent cells. They were further validated in celllines and tested in docetaxel-sensitive and resistant CRPCtumor samples (11FFPEand5OCTsamples) byqRT-PCR.Seven of the 18 marker genes were deregulated in thesame way in cell lines, FFPE and OCT samples. Theseincluded the CDH1 gene discussed above and also AREG(amphiregulin), DLC1, GJB2 (gap junction protein, b 2),IFIH1, MX1, and EPCAM, all of them consistently down-expressed in docetaxel-resistant tumor samples.Discordant results were observed in the expression of

other genes such as NEAT1 (nuclear paraspeckle assem-bly transcript 1), which was significantly downexpressedin docetaxel-resistant tumors but was within the top 10overexpressed genes in resistant cells. These conflictingresults could bedue to the limitations of the present study.First, gene panels derived from in vitro study cannotrepresent the complex biology andheterogeneity of tumor

cells in patients. Moreover, molecular changes derivedfrom tumor–stroma interaction are lost in in vitromodels.On the other hand, the range of docetaxel concentrationsin cultured cells differs fromdocetaxel levels in patients, afact that alsomay cause differences in gene expression. Ofnote, we found molecular alterations in docetaxel-resis-tant cells lines after long-term exposure to docetaxel. It isnot clear whether genetic alterations responsible for doc-etaxel resistance were already present in the initial cellpopulation or were induced after docetaxel exposure.Future comparison of the present gene expression ana-lysis with gene expression data from a de novo docetaxel-resistant cell line will be useful to further elucidatepathways involved in different resistance patterns. Inpatients, docetaxel resistance may exist before drugexposure (primary resistance) or may be developed aftera number of chemotherapy cycles (acquired resistance).In our series, where tumor samples were obtained beforechemotherapy exposure, we observed a concordancebetween some of the deregulated genes in the resistanttumors and previously observed results in resistant celllines. Thesedata suggest that basal gene expression altera-tions, not induced by treatment, may be responsible forthe survival of cancer cells in the presence of docetaxel.Finally, in the present workwe carried out an exploratoryanalysis in tumor samples that need to be further vali-dated in a larger cohort of patients.

In summary, this exploratory analysis provides infor-mation about potential genes and networks involved indocetaxel resistance in CRPC, as well as a basis for theinvestigation of the specific mechanism through whichthe TGF-b family protects cultured cells from docetaxel.The identification of docetaxel resistance genes may beuseful to select patientswhomaynot benefit from therapyor to develop targeted therapies to overcome docetaxelresistance. Further clinical validation of these results isneeded in patients with CRPC.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

The authors thank the Tumor Bank of the Department of Pathology oftheHospital Clı́nic for providing the tumor samples;M�onicaMarı́n for herexcellent technical assistance; Ana Rovira for scientific advice; and ElaineLilly, Ph.D., for correction of the English text.

Grant Support

This study was supported by the Fondo de Investigaciones Sanitarias(FIS 07/0388 and 08/0274) and RD07/0020/2014 grants. M. Marı́n-Agui-lera was supported by Cellex Foundation. Fondo de Investigaci�on Sani-taria, Instituto de Salud Carlos III (Spain), project grants PI070388,PI080274, and RD07/0020/2014.

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

Received April 18, 2011; revised September 14, 2011; accepted October12, 2011; published OnlineFirst October 25, 2011.






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2012;11:329-339. Published OnlineFirst October 25, 2011.Mol Cancer Ther Mercedes Marín-Aguilera, Jordi Codony-Servat, Susana G. Kalko, et al. Castration-Resistant Prostate CancerIdentification of Docetaxel Resistance Genes in

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