Identification of Docetaxel Resistance Genes in Castration-Resistant Prostate Cancer

Abstract

Docetaxel-based chemotherapy is the standard first-line therapy in metastatic castration-resistant prostate cancer (CRPC). However, most patients eventually develop resistance to this treatment. In this study, we aimed to identify key molecular genes and networks associated with docetaxel resistance in two models of docetaxel-resistant CRPC cell lines and to test for the most 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 showed differential expression of 243 genes (P < 0.05, Bonferroni-adjusted P values 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 deregulated 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-β superfamily in conferring drug resistance. Changes in expression of 18 selected genes were validated by real-time quantitative reverse transcriptase PCR in all four cell lines and tested in a set of 11 FFPE and five optimal cutting temperature tumor samples. Analysis in patients showed a noteworthy downexpression of CDH1 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.

Full text

Therapeutic Discovery
Identification of Docetaxel Resistance Genes in
Castration-Resistant Prostate Cancer
Mercedes Marín-Aguilera
1
, Jordi Codony-Servat
1
, Susana G. Kalko
2
, Pedro L. Fern
andez
3
,
Raquel Bermudo
5
, Elvira Buxo
1
, María Jos
e Ribal
4
, Pedro Gasc
on
1
, and Bego~
na Mellado
1
Abstract
Docetaxel-based chemotherapy is the standard first-line therapy in metastatic castration-resistant prostate
cancer (CRPC). However, most patients eventually develop resistance to this treatment. In this study, we aimed
to identify key molecular genes and networks associated with docetaxel resistance in two models of docetaxel-
resistant CRPC cell lines and to test for the most 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 showed differential expression of 243 genes (P<0.05, Bonferroni-adjusted Pvalues 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-bsuperfamily in
conferring drug resistance. Changes in expression of 18 selected genes were validated by real-time quantitative
reverse transcriptase PCR in all four cell lines and tested in a set of 11 FFPE and five optimal cutting temperature
tumor samples. Analysis in patients showed a noteworthy downexpression of CDH1 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 thatcancircumventsuchresistancetotreatment.
Mol Cancer Ther; 11(2); 329–39. 2011 AACR.
Introduction
Prostate cancer is the second most common cancer
among men worldwide (1). Approximately 85% of newly
diagnosed prostate cancer cases are localized to the pros-
tate, whereas the remainder are invasive or metastatic
disease (2). Patients with metastatic prostate cancer
respond initially to antiandrogen therapy. However,
tumors eventually progress and transform themselves
into castration-resistant prostate cancer (CRPC). Doce-
taxel improves survival of patients with metastatic CRPC
and is considered a standard first-line therapy in such
cases (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 microtubules
assembly and preventing depolymerization in the
absence of GTP (5). Furthermore, docetaxel leads to Bcl-
2 phosphorylation, which causes apoptosis of cancer cells
that had previously blocked the apoptotic-inducing
mechanism, leading to tumor regression (6). Some pro-
teins such as stathmin, Aurora-A, and b-III tubulin have
been described to be involved in resistance to antimicro-
tubule agents (7–9). However, mechanisms of resistance
to docetaxel in CRPC need to be further elucidated to
design targeted therapies that can circumvent treatment
resistance.
cDNA microarrays technology has given us the ability
to simultaneously examine the expression of thousands of
genes and determine the molecular profile of clinical
phenotypes, some of which can be involved in specific
cell profiles, such as chemotherapy resistance. This study
compared gene expression profiles of 2 models of doc-
etaxel-resistant CRPC cell lines, and identified a set of
genes involved in resistance to this chemotherapy. The
most differentially expressed genes were validated by
real-time quantitative reverse transcriptase PCR (qRT-
PCR) in cell lines and, in an exploratory way, in CRPC
tumor samples.
Authors' Affiliations:
1
Laboratory and Medical Oncology Department,
2
Bioinformatics Support Unit, Departments of
3
Pathology and
4
Urology,
Hospital Clínic; and
5
Human 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 Cancer
Therapeutics 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:
bmellado@clinic.ub.es
doi: 10.1158/1535-7163.MCT-11-0289
2011 American Association for Cancer Research.
Molecular
Cancer
Therapeutics
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Materials and Methods
Cell culture
The human prostate carcinoma cell lines DU-145 and
PC-3 (obtained from American Tissue Culture Collec-
tion) were converted to docetaxel-resistant cells by
exposing them to an initial dose of 5 nmol/L doce-
taxel and culturing surviving cells during 1 year and
6 months, respectively, with increasing doses in an
intermittent regimen. DU-145 and DU-145R cell lines
were cultured in RPMI-1640 medium (Invitrogen), and
PC-3 and PC-3R in F-12K nutrient mixture medium
(Invitrogen), both supplemented with 10% FBS. No
further authentication of the cell lines was done by the
authors. Docetaxel (Sigma) was dissolved in dimethyl
sulfoxide (10 mmol/L). IC
50
values were estimated in
triplicate from the dose–response curves.
Microarray hybridizations and differential
expression analysis
Total RNAs were isolated from DU-145, DU-145R,
PC-3, and PC-3R cell lines (in triplicate) using the TRIzol
Reagent (Invitrogen) according to the manufacturer’s
instructions. RNAs were purified using RNeasy Micro
kit (Qiagen), and quality and quantity were assessed on
a spectrophotometer. Fragmented, labeled, and ampli-
fied cDNA was hybridized to the Affymetrix Human
Genome U133Plus2.0 array, which represents about
38,500 well-characterized genes. Washes and scanning
of the arrays were carried out according to manufac-
turer’s instructions.
Raw expression measures were summarized after
background correction and normalization steps using
the robust multiarray analysis (RMA) methodology in
the affy (10) package from the Bioconductor project (11).
Unsupervised cluster analysis of high variability genes
was done with dChip v1.3 software (12). Differential
expression analysis was carried out by a linear model
using the empirical Bayes method to moderate the
standard errors of the estimated log ratio changes with
the limma package (13), according to adjusted Pvalues
less than 0.05 for the comparison of interest. Special
attention 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 analysis
Gene 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 understand
resistance mechanisms at a molecular level. Network-
eligible genes were placed by IPA into networks and
ranked by the relevance of the network-eligible molecules.
Patients and samples
Tumor samples from patients with metastatic prostate
cancer were collected to test microarray results in patients.
We selected patients who were prescribed docetaxel-
based therapy after the palliative transurethral deso-
bstructive resection, or after being biopsied once the
tumor had already spread. Samples were obtained before
the 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 samples
were stored at 80C until processing. The institutional
review board approved this study and written informed
consent was obtained from all patients.
FFPE samples belonged to 11 patients with CRPC, all of
whom had bone metastasis; 5 also had lymph node
lesions, 2 had lung metastasis, and 2 others had liver and
ureteral tumor growth. Six patients partially responded to
docetaxel and the other 5 progressed during the treat-
ment. On the other hand, OCT samples were extracted
from 5 patients with CRPC with bone metastasis, of whom
4 also had a lymph node lesion and 1 had liver metastasis.
Two patients partially responded to docetaxel and the
other progressed during the treatment.
Treatment response was evaluated by prostate-specific
antigen–based response criteria (15) and Response Eval-
uation Criteria in Solid Tumors (RECIST; ref. 16).
Gene validation in cell lines
Genes that could potentially be related with docetaxel
resistance were selected for further validation in cell lines
using 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 known path-
ways such as cell adhesion, cell signaling, or regulation
of apoptosis, using DAVID (17; 18) and IPA softwares (14).
Total RNAsfrom cell lines were isolated using the TRIzol
Reagent (Invitrogen) according to the manufacturer’s
instructions. One microgram of total RNA was reverse
transcribed using the High Capacity cDNA Archive Kit
(Applied Biosystems), following manufacturer’s instruc-
tions. qRT-PCR was carried out in a 7500 Real Time PCR
system (Applied Biosystems), according to the manufac-
turer’s recommendations. Data were acquired using SDS
Software 1.4. Expression values were based on the quan-
tification cycle (C
q
) from target genes relative to ACTB
endogenous control gene (DC
q
). Relative expression with
respect to each reference group studied was reported as
log ratio. Commercial codes for primers and probes from
target genes were AREG (amphiregulin-Hs00950669_m1),
CDH1 (E-cadherin-Hs01023895_m1), CYBRD1 (cyto-
chrome b-reductase 1-Hs00227411_m1), DLC1 (deleted
in liver cancer 1-Hs00183436_m1), GJB2 (gap junction
protein, b2-Hs00955889_m1), GSPT2 (G
1
to S-phase tran-
sition 2-Hs00250696_s1), IFIH1 (IFN induced with heli-
case C domain 1-Hs01070332_m1), IL8 (interleukin
8-Hs00174103_m1), MAPK13 (mitogen-activated protein
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kinase 13-Hs00559623_m1), MPZL2 (myelin protein zero-
like 2-Hs00170684_m1), MX1 (myxovirus resistance
1-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 adhesion
molecule-Hs00901885_m1), NEAT1 (nuclear paraspeckle
assembly transcript 1-Hs01008264_s1), and TNFAIP3 (tu-
mor necrosis factor, a-induced protein 3-Hs00234713_m1).
Gene expression in tumor samples
Tissues 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 FFPE
samples was obtained using the RecoverAll Total Nucleic
Acid Isolation Kit for FFPE (Ambion). The RNAs from
OCT samples were extracted using TRIzol Reagent (Invi-
trogen) according to the manufacturer’s instructions.
Quality and quantity of total RNAs were measured by
ND-1000 Spectrophotometer (Nanodrop Technologies).
The genes selected for microarrays validation in cell
lines were also tested in tumor samples following the
same qRT-PCR protocol except that the expression values
of target genes were relative to the C
q
mean of ACTB,B2M,
and GUSB endogenous genes.
Viability assay
TGFB1 (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) were maintained during 7 consecutive
days with TGFB1 (5 ng/mL) in the medium. Parental cells
with and without sustained treatment were then seeded at
a density of 3,200 cells per well in a 96-well microtiter plate
in the corresponding medium with 10% FBS. After 24
hours, cells were exposed to docetaxel with/without
TGFB1 for an additional 72 hours. Finally, cell prolifera-
tion/viability was assessed by using MTT colorimetric
assay (Promega).
Statistical analysis
Changes in gene expression comparing docetaxel-
resistant and docetaxel-sensitive cell lines and tumor
samples by qRT-PCR were analyzed with Wilcoxon
rank-sum test, considering as positively validated those
genes with significant expression changes (P<0.05).
SPSS 12.0 software was used for statistical analyses.
Results
Docetaxel-resistant cell lines
DU-145R and PC-3R cells acquired levels of resistance
to docetaxel that was 2 to 5 times higher than their parent
cells. IC
50
values for DU-145R and PC-3R ranged from 10
to 15 and 20 to 22 nmol/L, respectively, whereas DU-145
and PC-3 registered from 4 to 5 and 3 to 5 nmol/L,
respectively (data not shown).
Differentially expressed genes in resistant cells
Initially, unsupervised clustering analysis of microar-
rays was carried out using the probe sets that varied most
throughout the whole experiment, and this exploration
revealed a good segregation of the arrays in their respec-
tive classes on the basis of expression values (data not
shown). Subsequent differential expression analysis
revealed 1,064 and 1,361 differentially expressed genes
(Bonferroni <0.05 and log ratio >1.2), equivalent to 1,710
and 2,117 Affymetrix probe sets, between DU-145R versus
DU-145 and PC-3R versus PC-3, respectively (Supple-
mentary Table SI). Top 20 over- and downexpressed genes
from docetaxel-resistant cells with respect to their parent
cells according to log ratio expression values, and their
main function, are summarized in Supplementary Table
SII. When comparing differentially expressed genes
between both resistant cell lines (DU-145R and PC-3R),
243 genes overlapped (Supplementary Table SIII). From
those genes, 172 were over- or downexpressed in both
DU-145R and PC-3R cells versus their parent cells. The top
10 commonly over- and downexpressed genes, with their
respective main function, are summarized in Table 1.
Ingenuity network analysis
Only considering probe sets common to both resistant
cell lines, IPA qualified 252 genes as network and function
eligible. A core analysis showed 12 networks on the basis
of this gene selection with a score more than 2. The 2 top
networks were detected with scores of 51 and 39 respec-
tively, and were the same for DU-145R and PC-3R cell
lines (Fig. 1). Specifically, network 1 (Fig. 1A) was centered
on, amongst others, the nuclear receptor PPARA (perox-
isome proliferator-activated receptor a). This gene direct-
ly interacts with an important complex for cell survival
and proliferation, NFKB, which in turn interacts with
TNFAIP3,HMGA1 (high mobility group AT-hook 1), and
ISG15 (ISG15 ubiquitin-like modifier). Other deregulated
genes directly related to PPARA are SOCS2 (suppressor of
cytokine signaling 2), ASL (argininosuccinate lyase),
ASNS (asparagine synthetase), ELOVL6 (ELOVL family
member 6), IGFBP6 (insulin-like growth factor binding
protein 6) and CITED2 (Cbp/p300-interacting transacti-
vator, with Glu/Asp-rich carboxy-terminal domain 2).
Other genes commonly deregulated in network 1 are
SERPINA1 (serpin peptidase inhibitor, clade A), VDR
(vitamin D receptor), ITGB2 (integrin, b2), and TGFBR3
(TGF-breceptor III, also known as betaglycan; Fig. 1A).
The last is overexpressed in both DU-145R and PC-3R
cells. Interestingly, other TGF-bmembers appear deregu-
lated in the same network, such as TGFB2, which is
differentially regulated in both cell lines, and TGF-b
ligand, which is included by the software as a link
between the former TGF-belements and LTBP2 (latent
TGF-bbinding protein 2).
Network 2 (Fig. 1B) is mainly centered on the CDH1
gene, which codifies a classical calcium dependent cell–
cell adhesion glycoprotein. MYO6 (myosin VI), ID2
(inhibitor of DNA binding 2), PTPRM (protein tyrosine
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Table 1. Top 10 commonly over- and downexpressed genes in DU-145R and PC-3R versus their parent cells, with their respective main function
and log ratio expression
Overexpressed
DU-145R vs. DU-145 PC-3R vs. PC-3
Gene symbol Log ratio Main function Gene symbol Log ratio Main function
GSPT2 4.487 GTPase NEAT1 3.468 Non–protein coding
ID2 4.050 Multicellular organism development;
regulation of transcription
FLJ27352 3.287 Undefined
CXCR7 3.879 Signal transduction TGFBR3 2.960 Epithelial to mesenchymal transition; signal
transduction; cell cycle; cell migration
NDRG1 3.233 Stress and hormone responses,
cell growth and differentiation
PLSCR4 2.552 Phospholipid scrambling
FRMD3 3.191 Structural protein HTRA1 2.389 Regulation of cell growth
TP53INP1 2.797 Cell-cycle arrest; induction of apoptosis S100A4 2.217 Epithelial to mesenchymal transition;
cell-cycle progression and differentiation.
GOSR2 2.434 Intracellular protein transport CYBRD1 2.158 Transport; oxidation reduction
CITED2 2.372 Vasculogenesis; response to hypoxia;
positive regulation of cell–cell adhesion;
positive regulation of cell cycle
PPP2R2C 2.052 Signal transduction
KLHL24 2.309 Undefined GSPT2 2.006 GTPase
NEAT1 2.267 Non–protein coding PC 1.979 Metabolism, insulin secretion, and synthesis
of the neurotransmitter glutamate
Downexpressed
SCEL –6.428 Epidermis development; assembly
or regulation of structural proteins
ESRP1 –6.612 Regulation of RNA splicing
EPCAM –5.357 Calcium-independent cell adhesion TACSTD2 –6.309 Signal transduction
C1orf116 –5.292 Undefined EFEMP1 –6.079 Extracellular matrix glycoprotein
TACSTD2 –5.240 Signal transduction GJB2 –5.984 Transport; cell–cell signaling
SERPINA1 –5.153 Response to hypoxia MPZL2 –5.088 Cell adhesion
AIM1 –4.876 Undefined RBM47 –4.333 Undefined
AREG –4.864 EGF receptor signaling pathway;
cell proliferation; cell–cell signaling
JPH1 –4.312 Calcium ion transport into cytosol
JPH1 –3.992 Calcium ion transport into cytosol TXNIP –4.224 Transcription; response to oxidative
stress; regulation of cell proliferation
GPR87 –3.648 Signal transduction GPR87 –4.192 Signal transduction
GALNT3 –3.501 Carbohydrate metabolic process KAP2.1B –4.181 Keratin-associated protein
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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 in
resistant 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.
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phosphatase, receptor type M), and OCLN (occludin)
are other deregulated genes directly related with
CDH1.
No overlapping genes were found between networks 1
and 2, although both are involved in gene expression.
Functional in silico analysis
IPA detected several biologic functions with overrep-
resentation, when considering together the differentially
expressed genes in docetaxel-resistant versus their parent
cells. Such functions were related with elemental metab-
olism of cell survival like gene expression, cell growth and
proliferation, cell cycle, cell death, and cell movement.
Specifically, and as expected, the 2 main networks were
involved in cell growth and proliferation, but also in
gene expression, cell movement and development, and
the skeletal and muscular system.
The top overexpressed individual genes were enzymes,
cell surface receptors, transcription regulators, and struc-
tural proteins involved in cell transport, signaling, bind-
ing, and cytoskeletal organization. Similarly, proteins
from 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-bfamily
The TGF-bsuperfamily was represented at network 1
and was overexpressed in resistant cell lines. We selected
this target for a functional study of docetaxel resistance.
The results of MTT experiments showed that continuous
treatment with TGFB1 (5 ng/mL) increased DU-145 and
PC-3 cell viability until 16.5% and 15.6%, respectively,
with respect to cells that were not cultured with 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 in
cells without TGFB1 treatment. Cell viability of DU-145
cells was not affected by TGFB1 under these experimental
conditions (Fig. 2).
Validation by qRT-PCR in cell lines and test in tumor
samples
A panel of 18 of the top 20 with the highest or lowest
expression range in both DU-145R and PC-3R versus
parent cells lines (see materials and methods) was selected
for 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 (n¼5) versus
docetaxel-sensitive (n¼6) FFPE tumor samples, and
docetaxel-resistant (n¼3) versus docetaxel-sensitive
(n¼2) OCT tumor samples from patients with metastatic
CRPC. This analysis showed a significant downexpres-
sion of NEAT1 (P¼0.044) in FFPE tumors. Of the 18
markers studied, 10 markers in the FFPE samples and 13
in OCT samples were deregulated in the same way as in
the in vitro models. Interestingly, as shown in Fig. 4, 7 of
these marker genes were commonly downexpressed in
docetaxel-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 networks
involved 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
% cell viability
120
100
80
60
40
20
0
% cell viability
120
100
80
60
40
20
0
% cell viability
120
100
80
60
40
20
0
% cell viability
5 7.5 10 20
Docetaxel (nmol/L)
Ctrl. 0 1 1.8 2.5 5 7.5 10 20
Docetaxel (nmol/L)
Ctrl. 0 1 1.8 2.5 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 of
docetaxel treatment for 72 hours in
DU-145 and PC-3 cells. A, after a
sustained treatment with TGFB1
5 ng/mL during 7 days plus
additional 72 hours and after
72 hours of treatment with TGFB1
10 ng/mL (B). Cell viability is
expressed as the relative
colorimetric signal calculated from
the untreated wells (Ctrl.). Each
experimental condition was carried
out in triplicate (SD is shown).
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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 for
DU-145R and PC-3R, respectively). The fact that docetaxel
IC
50
in resistant cells was not substantially higher than in
sensitive cells is consistent with other in vitro docetaxel
resistance studies (19; 20). This issue may be partially
explained by the fact that microtubule alterations induced
by 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 line
models. We found 243 eligible genes (Bonferroni <0.05
and 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 as
gene expression and cell growth, proliferation, death, and
movement. Interestingly, in both cell lines GSPT2 and
NEAT1 were within the top 10 overexpressed genes, and
TACSTD2 (tumor-associated calcium signal transducer
2), JPH1 (junctophilin 1), and GPR87 (G-protein–coupled
receptor 87) within the top 10 downexpressed genes.
Among these genes, only TACSTD2 and GPR87 have
already been related with cancer in the literature (22; 23).
Briefly, GSPT2 encodes a GTPase that may be involved
in mRNA stability and NEAT1 is a non–protein-coding
RNA that seems to regulate mRNA export. Such involve-
ment of docetaxel with mRNA regulation is not surprising
because, as previously described, taxanes can promote
transcription and mRNAs stabilization of some genes.
Such stabilizing effects can be achieved by the stimulation
of proteins that bind the AU-rich region of the 30UTR
region of target genes (24).
On the other hand, TACSTD2 encodes a carcinoma-
associated antigen that is a cell surface receptor that
transduces calcium signals. It has been described to be
unmethylated in normal prostate cells and prostatic
intraepithelial neoplasia but hypermethylated in primary
prostate tumors (25), suggesting that methylation could
be one of the mechanisms for silencing the expression of
crucial genes, thus inactivating the apoptotic pathway in
CRCP. The real significance of TACSTD2 intraexpression
in resistant cell lines and the potential role of docetaxel in
methylation-based regulation need to be further explored.
0.5
0
–0.5
–1
–1.5
–2
Genes
AREG
CDH1
CYBRD1
DLC1
GJB2
GSPT2
IFIH1
IL8
MAPK13
MPZL2
MX1
MYO6
S100A4
SERPINA1
SYK
EPCAM
NEAT1
TNFAIP3
Genes
AREG
CDH1
CYBRD1
DLC1
GJB2
GSPT2
IFIH1
IL8
MAPK13
MPZL2
MX1
MYO6
S100A4
SERPINA1
SYK
EPCAM
NEAT1
TNFAIP3
FFPE tumor samples
OCT tumor samples
Log ratio
1.5
1
0.5
0
–0.5
–1
–1.5
–2
–2.5
–3
–3.5
Log ratio
Figure 4. Differential gene expression by qRT-PCR in docetaxel-resistant
versus docetaxel-sensitive FFPE and OCT tumor samples from patients
with metastatic CRPC. Expression data are represented by a log ratio
calculated comparing DC
q
from docetaxel-resistant patients with the
median of DC
q
from docetaxel-sensitive patients. DC
q
was calculated as
the difference between C
q
of target genes and the mean of C
q
of the
endogenous control genes ACTB,B2M,andB-GUS.,P<0.05.
6
4
2
0
–2
–4
–6
–8
Validated genes
AREG
CDH1
CYBRD1
DLC1
GJB2
GSPT2
IFIH1
IL8
MAPK13
MPZL2
MX1
MYO6
S100A4
SERPINA1
SYK
EPCAM
NEAT1
TNFAIP3
DU-145R vs. DU-145
Log ratio
6
4
2
0
–2
–4
–6
–8
Validated genes
AREG
CDH1
CYBRD1
DLC1
GJB2
GSPT2
IFIH1
IL8
MAPK13
MPZL2
MX1
MYO6
S100A4
SERPINA1
SYK
EPCAM
NEAT1
TNFAIP3
PC-3R vs. PC-3
Log ratio
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 represented
by a log ratio calculated by comparing DC
q
from resistant cells with
DC
q
from parent cells. DC
q
was calculated as the difference between C
q
of target genes and C
q
of the endogenous control gene ACTB.
Docetaxel Resistance in Castoration-Resistant Prostate Cancer
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Another downexpressed gene was JPH1, which med-
iates cross-talk between the cell surface and intracellular
ion channels, modulating electrochemical gradients that
are vital to the cell and a potentially strong influence on
drug activity, according to previous work (26). According
to the authors, the expression of several genes that encode
subunits of sodium, chloride, potassium, and other cation
channels also is correlated with drug activity.
GPR87 encodes a G-protein–coupled receptor that
plays an essential role in many physiologic processes,
including neurotransmission, immunity, and inflamma-
tion. This gene seems to be overexpressed in diverse
carcinomas and plays an essential role in tumor cell
survival. A recent study revealed that a lack of GPR87
triggers an increase in p53, concomitant with a decrease in
AKT, which results in the sensitization of tumor cells to
DNA damage–induced apoptosis and growth suppres-
sion (23). However, these results are in disagreement with
the fact that resistant cells downregulate the expression of
GPR87 but remain able to survive and proliferate. The
role of several other genes that contribute to docetaxel
resistance must account for such discrepancy.
Interestingly, several well-known molecules already
related with docetaxel resistance have been found to be
deregulated in the present work. ABCA13,ABCA8, and
ABCC2, all from the ABC (ATP-binding cassette) family of
drug transporters, are significantly deregulated in DU-
145R cells; only ABCC2 was already known to be involved
in multidrug resistance (27). None of these genes has been
found to be deregulated in both resistant cell lines. On the
other hand, there are growing evidences about the role of
b-tubulin isotypes in resistance to taxanes in CRPC, as
they are the primary target of these drugs (9; 28). In the
present study we observed that TUBB2B (tubulin, b2B)
was downexpressed in PC-3R cells versus their parent cell
line (see Supplementary Table SI), but no differences were
observed in the expression of b-tubulins in the DU-145
model. Furthermore, tubulin is one of the molecules in-
cluded by the Ingenuity software as a link between EML1
and MAP2, actin and calmodulin proteins (Fig. 1B);
however, its expression was not significantly deregulated.
These results suggest that alternative pathways may be
more important in generating docetaxel resistance than
those directly related to tubulin alterations.
Apart from an individual view of deregulated genes, in
this study we focused on IPA analysis that allowed us to
obtain global and integrated molecular information about
interactions between significant differentially expressed
genes. Results from such analysis showed that the most
significant network was centered on PPARA, which was
overexpressed in docetaxel-resistant cells (Fig. 1A).
PPARA is a nuclear receptor that regulates the expression
of multiple genes involved in cell proliferation, differen-
tiation, and immune and inflammation responses. The
alpha form of this gene is functional in human prostate
and is downregulated by androgens. It has been sug-
gested that its overexpression in advanced prostate cancer
indicates a role in tumor progression, with the potential
involvement of dietary factors (29). As shown in Fig. 1A,
PPARA interacts with CITED2 (Cbp/p300-interacting
transactivator, with Glu/Asp-rich carboxy-terminal
domain 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 a
variety of human cancers and might play an important
role in carcinogenesis and/or the progression of human
prostate cancer (31). However, no link between ETS1
expression and docetaxel resistance has been described
to date.
ETS1 directly interacts with VDR and ITGB2,which
also are downexpressed. VDR is a transacting transcrip-
tional regulatory factor involved in a variety of meta-
bolic pathways, but also in antineoplastic activities. In
fact, calcitriol, the most active metabolite of vitamin D,
showed to enhance antitumor activity of docetaxel,
although an effect on patients survival has not been
shown (32; 33). Downregulation of ITGB2 has been
previously associated with transition between prostatic
intraepithelial neoplasia and prostate cancer, playing a
role in cell adhesion of invasive prostate cancer cells
(34).
A second focus of interest in network 1 is NFKB. This
protein complex has been widely associated with onco-
genesis due to its ability to regulate cell proliferation and
protect 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-sensitive
LNCaP and LAPC-4 cells, which is consistent with the
role of NFKB in progression of prostate cancer. Moreover,
higher levels of NFkB protein can be further enhanced in
response to certain types of chemotherapy (36; 37). Our
group previously found that the inhibition of this complex
may be an attractive strategy to enhance docetaxel
response in PC (38). This strategy has also been clinically
tested through the use of bortezomib (39; 40).
The present study specifically explored the role of the
TGF-bsuperfamily, which is also represented at network
1. The signaling pathway derived from this family of
proteins has a principal role in growth control (41). As
a member of this family, TGFBR3 acts as a coreceptor of
TGF-bligand, being a positive or negative regulator of
TGF-bsignals depending on the cellular context (42; 43).
Recently, it has been shown that TGFBR3 may act as a
protective factor in the apoptotic process of fibroblasts by
negative regulation of TGF-bsignaling (44). Our results
further suggest that TGFB1 acts as a protective factor
against docetaxel for DU-145 and PC-3 cells and has a
partial role in the development of docetaxel resistance in
cultured cells. The specific mechanism through which
TGF-b/TGFBR3 protects DU-145 and PC-3 cells from this
drug remains to be elucidated.
The epithelial cell adhesion molecule CDH1, which is
significantly downexpressed in docetaxel-resistant cells,
is included in the second most significant network
obtained in this analysis (Fig. 1B). The loss of CDH1 in
Marín-Aguilera et al.
Mol Cancer Ther; 11(2) February 2012 Molecular Cancer Therapeutics
336
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metastatic cells has been shown in a variety of in vitro and
in vivo models, and has been related to the epithelial-
mesenchymal transition process (45–47). However, its
role in chemotherapy resistance should be further stud-
ied. The mere loss of CDH1 probably does not directly
confer chemoresistance properties to the tumor cell, but
signals may be conveyed that induce resistance to che-
motherapy. According to network 2, CDH1 directly inter-
acts with the transcription regulator ID2 (inhibitor of
DNA binding 2), which is significantly overexpressed
in resistant cells, and enzymes like OCLN, which is also
involved in cell–cell adhesion and is downexpressed in
resistant cells. Other CDH1-related genes in this network
are the phosphatase PTPRM, the plakophilin PKP2, and
the 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 DNA
binding, transcriptional regulation, and protein–protein
interactions. It is known that this protein modulates P53
function and inhibits cell growth in the RAS/RAF signal-
ing pathway, an antitumor activity. Downexpression of
this gene in resistant cells seems to be related to their
ability to grow despite the presence of docetaxel in the
medium.
A recent study used microarray analysis to compare
the PC-3R cell line with and without docetaxel in culture
media, 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-145
with docetaxel-resistant DU-145 cells and showed a set
of 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 escape
in prostate cancer (48), was represented in both models.
Methodologic factors and differences in bioinformatics
analysis between these studies could account for such
high variability of commonly deregulated genes in
docetaxel-resistant cells.
Eighteen genes were selected according to their func-
tion and degree of relative expression in resistant cells
versus parent cells. They were further validated in cell
lines and tested in docetaxel-sensitive and resistant CRPC
tumor samples (11 FFPE and 5 OCT samples) by qRT-PCR.
Seven of the 18 marker genes were deregulated in the
same way in cell lines, FFPE and OCT samples. These
included the CDH1 gene discussed above and also AREG
(amphiregulin), DLC1,GJB2 (gap junction protein, b2),
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 downexpressed
in docetaxel-resistant tumors but was within the top 10
overexpressed genes in resistant cells. These conflicting
results could be due to the limitations of the present study.
First, gene panels derived from in vitro study cannot
represent the complex biology and heterogeneity of tumor
cells in patients. Moreover, molecular changes derived
from tumor–stroma interaction are lost in in vitro models.
On the other hand, the range of docetaxel concentrations
in cultured cells differs from docetaxel levels in patients, a
fact that also may cause differences in gene expression. Of
note, we found molecular alterations in docetaxel-resis-
tant cells lines after long-term exposure to docetaxel. It is
not clear whether genetic alterations responsible for doc-
etaxel resistance were already present in the initial cell
population 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 elucidate
pathways involved in different resistance patterns. In
patients, docetaxel resistance may exist before drug
exposure (primary resistance) or may be developed after
a number of chemotherapy cycles (acquired resistance).
In our series, where tumor samples were obtained before
chemotherapy exposure, we observed a concordance
between some of the deregulated genes in the resistant
tumors and previously observed results in resistant cell
lines. These data suggest that basal gene expression altera-
tions, not induced by treatment, may be responsible for
the survival of cancer cells in the presence of docetaxel.
Finally, in the present work we carried out an exploratory
analysis 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 in
docetaxel resistance in CRPC, as well as a basis for the
investigation of the specific mechanism through which
the TGF-bfamily protects cultured cells from docetaxel.
The identification of docetaxel resistance genes may be
useful to select patients who may not benefit from therapy
or to develop targeted therapies to overcome docetaxel
resistance. Further clinical validation of these results is
needed 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 of
the Hospital Clı
´nic for providing the tumor samples; M
onica Marı
´n for her
excellent technical assistance; Ana Rovira for scientific advice; and Elaine
Lilly, 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. Marı
´n-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 the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received April 18, 2011; revised September 14, 2011; accepted October
12, 2011; published OnlineFirst October 25, 2011.
Docetaxel Resistance in Castoration-Resistant Prostate Cancer
www.aacrjournals.org Mol Cancer Ther; 11(2) February 2012 337
on November 5, 2015. © 2012 American Association for Cancer Research. mct.aacrjournals.org Downloaded from
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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 Cancer
Identification of Docetaxel Resistance Genes in
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