VAV3 mediates resistance to breast cancer endocrine therapy

VAV3 mediates resistance to breast cancer endocrine therapy
VAV3 mediates resistance to breast cancer
endocrine therapy
Helena Aguilar, Ander Urruticoechea, Pasi Halonen, Kazuma Kiyotani, Taisei Mushiroda,
Xavier Barril, Jordi Serra-Musach, Abul Islam, Livia Caizzi, Luciano Di Croce, Ekaterina
Nevedomskaya, Wilbert Zwart, Josefine Bostner, Elin Karlsson, Gizeh Perez-Tenorio,
Tommy Fornander, Dennis C Sgroi, Rafael Garcia-Mata, Maurice Phm Jansen, Nadia García,
Núria Bonifaci, Fina Climent, María Teresa Soler, Alejo Rodríguez-Vida, Miguel Gil, Joan
Brunet, Griselda Martrat, Laia Gómez-Baldó, Ana I Extremera, Agnes Figueras, Josep Balart,
Robert Clarke, Kerry L Burnstein, Kathryn E Carlson, John A Katzenellenbogen, Miguel
Vizoso, Manel Esteller, Alberto Villanueva, Ana B Rodríguez-Peña, Xosé R Bustelo, Yusuke
Nakamura, Hitoshi Zembutsu, Olle Stål, Roderick L Beijersbergen and Miguel Angel Pujana
Linköping University Post Print
N.B.: When citing this work, cite the original article.
Original Publication:
Helena Aguilar, Ander Urruticoechea, Pasi Halonen, Kazuma Kiyotani, Taisei Mushiroda,
Xavier Barril, Jordi Serra-Musach, Abul Islam, Livia Caizzi, Luciano Di Croce, Ekaterina
Nevedomskaya, Wilbert Zwart, Josefine Bostner, Elin Karlsson, Gizeh Perez-Tenorio,
Tommy Fornander, Dennis C Sgroi, Rafael Garcia-Mata, Maurice Phm Jansen, Nadia García,
Núria Bonifaci, Fina Climent, María Teresa Soler, Alejo Rodríguez-Vida, Miguel Gil, Joan
Brunet, Griselda Martrat, Laia Gómez-Baldó, Ana I Extremera, Agnes Figueras, Josep Balart,
Robert Clarke, Kerry L Burnstein, Kathryn E Carlson, John A Katzenellenbogen, Miguel
Vizoso, Manel Esteller, Alberto Villanueva, Ana B Rodríguez-Peña, Xosé R Bustelo, Yusuke
Nakamura, Hitoshi Zembutsu, Olle Stål, Roderick L Beijersbergen and Miguel Angel Pujana,
VAV3 mediates resistance to breast cancer endocrine therapy, 2014, Breast Cancer Research,
(16), 3, R53.
Copyright: BioMed Central
Postprint available at: Linköping University Electronic Press
Aguilar et al. Breast Cancer Research 2014, 16:R53
Open Access
VAV3 mediates resistance to breast cancer
endocrine therapy
Helena Aguilar1, Ander Urruticoechea1,26, Pasi Halonen2, Kazuma Kiyotani3, Taisei Mushiroda3, Xavier Barril4,5,
Jordi Serra-Musach1,6, Abul Islam7, Livia Caizzi8,9, Luciano Di Croce5,8,9, Ekaterina Nevedomskaya10, Wilbert Zwart10,
Josefine Bostner11, Elin Karlsson11, Gizeh Pérez Tenorio11, Tommy Fornander12, Dennis C Sgroi13, Rafael Garcia-Mata14,
Maurice PHM Jansen15, Nadia García16, Núria Bonifaci1, Fina Climent17, María Teresa Soler17, Alejo Rodríguez-Vida18,
Miguel Gil18, Joan Brunet6, Griselda Martrat1, Laia Gómez-Baldó1, Ana I Extremera1, Agnes Figueras16, Josep Balart16,
Robert Clarke19, Kerry L Burnstein20, Kathryn E Carlson21, John A Katzenellenbogen21, Miguel Vizoso22,
Manel Esteller5,22,23, Alberto Villanueva16, Ana B Rodríguez-Peña24, Xosé R Bustelo24, Yusuke Nakamura3,25,
Hitoshi Zembutsu25, Olle Stål11, Roderick L Beijersbergen2 and Miguel Angel Pujana1*
Introduction: Endocrine therapies targeting cell proliferation and survival mediated by estrogen receptor α (ERα)
are among the most effective systemic treatments for ERα-positive breast cancer. However, most tumors initially
responsive to these therapies acquire resistance through mechanisms that involve ERα transcriptional regulatory
plasticity. Herein we identify VAV3 as a critical component in this process.
Methods: A cell-based chemical compound screen was carried out to identify therapeutic strategies against resistance
to endocrine therapy. Binding to ERα was evaluated by molecular docking analyses, an agonist fluoligand assay and
short hairpin (sh)RNA–mediated protein depletion. Microarray analyses were performed to identify altered gene
expression. Western blot analysis of signaling and proliferation markers, and shRNA-mediated protein depletion in
viability and clonogenic assays, were performed to delineate the role of VAV3. Genetic variation in VAV3 was assessed
for association with the response to tamoxifen. Immunohistochemical analyses of VAV3 were carried out to determine
its association with therapeutic response and different tumor markers. An analysis of gene expression association with
drug sensitivity was carried out to identify a potential therapeutic approach based on differential VAV3 expression.
Results: The compound YC-1 was found to comparatively reduce the viability of cell models of acquired resistance.
This effect was probably not due to activation of its canonical target (soluble guanylyl cyclase), but instead was likely a
result of binding to ERα. VAV3 was selectively reduced upon exposure to YC-1 or ERα depletion, and, accordingly, VAV3
depletion comparatively reduced the viability of cell models of acquired resistance. In the clinical scenario, germline
variation in VAV3 was associated with the response to tamoxifen in Japanese breast cancer patients (rs10494071
combined P value = 8.4 × 10−4). The allele association combined with gene expression analyses indicated that low
VAV3 expression predicts better clinical outcome. Conversely, high nuclear VAV3 expression in tumor cells was
associated with poorer endocrine therapy response. Based on VAV3 expression levels and the response to erlotinib in
cancer cell lines, targeting EGFR signaling may be a promising therapeutic strategy.
Conclusions: This study proposes VAV3 as a biomarker and a rationale for its use as a signaling target to prevent
and/or overcome resistance to endocrine therapy in breast cancer.
* Correspondence: miguel[email protected]
Breast Cancer and Systems Biology Unit, Translational Research Laboratory,
Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical
Research (IDIBELL), Avda. Gran via 199, L’Hospitalet del Llobregat, Barcelona
08908, Catalonia, Spain
Full list of author information is available at the end of the article
© 2014 Aguilar et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver ( applies to the data made available in this article,
unless otherwise stated.
Aguilar et al. Breast Cancer Research 2014, 16:R53
Endocrine therapies are the cornerstone of the curative
and palliative treatment of ERα-positive breast cancer.
However, even patients who initially respond to these
therapies may eventually develop resistance. Current
knowledge of the molecular mechanisms of acquired resistance to endocrine therapies suggests a model in which
crosstalk between ERα and growth factor signaling pathways plays an important role [1-3]. There may also be
resistance mechanisms partially or totally independent of
growth factor signaling, such as mutations in the ESR1
gene, which encodes for ERα, that alter ligand and/or
coactivator binding [4-6].
Beyond the alterations in growth factor signaling pathways identified to date, the binding plasticity of ERα to
chromatin is central in therapeutic resistance and cancer
progression [7]. This plasticity is mediated by the interaction of ERα with FOXA1 and, importantly, as a result,
a rewired transcriptional program that endorses resistance
[8]. In this scenario, however, it is not fully understood
which transcriptional outputs—possibly those involved in
growth factor signaling pathways—may be critical in the
acquisition of the resistant phenotype.
In recent years, different breast cancer cell models
have been generated in efforts to decipher the mechanisms of acquired resistance to endocrine therapies
[3,9,10]. One popular model was based on the long-term
estrogen deprivation (LTED) of the ERα-positive breast
cancer cell line MCF7 [11-14]. This model was designed
to recapitulate the effects of the therapeutic use of aromatase inhibitors (AIs) in postmenopausal breast cancer
[15]. Relevant differences, but also similarities, have been
described between the MCF7-LTED model and other
cell models of acquired resistance [16,17]. Although this
observation raises potential limitations, the results obtained with these models should be evaluated in the
corresponding clinical settings. In our present study,
in which we start with an analysis of the response of
MCF7-LTED cells to different small compounds and
then report our testing of predictions in different cohorts
of breast cancer patients, we propose that VAV3/VAV3 is
a key ERα-downstream determinant of the response to
endocrine therapies.
Cell culture and viability assays
MCF-7 cells were routinely cultured and maintained in
Roswell Park Memorial Institute medium containing
10% fetal bovine serum and 2 mM glutamine. MCF7LTED cells were established in phenol red-free medium
containing 10% dextran-coated, charcoal-stripped serum
[17]. All other cell lines used in this study were cultured
according to standard protocols [18]. The epidermal
growth factor (EGF) (Sigma-Aldrich, St Louis, MO, USA)
Page 2 of 16
was used at 10 ng/ml for 5 minutes. Cellular viability
was evaluated using standard methylthiazol tetrazolium
(MTT)–based assays (Sigma-Aldrich). The results of these
assays are expressed relative to vehicle-treated controls
and to the original time point.
Chemical compound screen
MCF7 and MCF7-LTED cells were plated in 384-well
microtiter plates, and five compound dilutions (1 nM to
10 μM final concentration) from the Library of Pharmacologically Active Compounds (LOPAC1280) (1,258 compounds; Sigma-Aldrich) were added to the cultures. Cell
viability was assessed after 72 hours using MTT-based
assays and the EnVision spectrofluorometer (PerkinElmer,
Waltham, MA, USA). The screen was performed in triplicate. Data quality was assessed (Z′-factor > 0.5 for all
screens), and data analysis was performed using the
cellHTS2 module in the Screensaver database [19]. The
data were normalized between 0 and 1 using positive
(1 μM phenylarsene oxide) and negative (0.1% dimethyl
sulfoxide (DMSO)) controls. For hit selection, the difference between the normalized percentage inhibition
(NPI) in MCF7 and MCF7-LTED cells was calculated
by subtraction (ΔNPI = NPI(MCF7-LTED) − NPI(MCF7)),
and the differentials were clustered with the MeV software
package [20] using the Cluster Affinity Search method
with the Euclidean distance metric (threshold of 0.7).
Based on the 18 clustered differential profiles, 83% of the
compounds (n = 1,047) had no differential effect between
the cell lines, 1% (n = 13) were more selective towards
MCF7-LTED cells and 0.5% (n = 6) were more selective
toward MCF7 cells. The YC-1 compound was purchased
from Sigma-Aldrich and from Chemgen Pharma International (custom synthesis order; Calcutta, India), and
erlotinib was purchased from Santa Cruz Biotechnology
(Santa Cruz, CA, USA).
cGMP, subcellular fractionation, and Western blotting
The cGMP levels were measured using the Amersham
cGMP Direct Biotrak EIA system (GE Healthcare Life
Sciences, Pittsburgh, PA, USA). Fractionation was performed with a subcellular protein fraction kit (Thermo Fisher
Scientific, Asheville, NC, USA). Cells were lysed in buffer
containing 50 mM Tris-HCl pH 8, 0.5% Nonidet P-40,
100 mM NaCl and 0.1 mM ethylenediaminetetraacetic
acid, supplemented with protease inhibitor cocktail (Roche
Molecular Biochemicals, Indianapolis, IN, USA) and 1 mM
NaF. Lysates were clarified twice by centrifugation at
13,000 × g, and protein concentration was measured
using the Bradford method (Bio-Rad Laboratories,
Hercules, CA, USA). Lysates were resolved in SDS-PAGE
gels and transferred to Immobilon-P membrane (EMD
Millipore, Billerica, MA, USA) or polyvinylidene fluoride
membrane (Roche Molecular Biochemicals), and target
Aguilar et al. Breast Cancer Research 2014, 16:R53
proteins were identified by detection of horseradish
peroxidase–labeled antibody complexes with chemiluminescence using an Amersham ECL Western Blotting
Detection Kit (GE Healthcare Life Sciences).
ERα structural analysis and binding assay
Chains A and C of the RCSB Protein Data Bank (PDB)
structure 3OS8 [Swiss-Prot:P03372] were superimposed
and used as representative structures of the partially
constrained and unconstrained forms, respectively. Hydrogen atoms and protonation states were automatically
assigned using the Protonate 3D function of the Molecular
Operating Environment (Chemical Computing Group,
Montreal, QC, Canada) [21], and the structures were
saved in Mol2 file format, which was then used as input
for docking analysis in rDock [22]. The cavity was defined
as the available space 6 Å around the crystallized ligand.
Both WAY6 and YC-1 were docked to each of the conformations in exhaustive sampling mode (100 genetic
algorithm runs). The binding mode in chain A (binding
mode 1, as previously described [23]) was considered to
be responsible for the partial agonist activity, and the
binding mode in chain C (binding mode 4, as previously
described [23]) caused a shift in the conformation of
helices 3 and 11, which displaced helix 12 and resulted in
an inactive state. To test the performance of the docking
program, WAY6 bound to chain C was cross-docked to
chain A, and vice versa. The experimental binding mode
of WAY6 was reproduced in both cases, although modes
1 and 4 scored very similarly in chain C, suggesting that
these modes can coexist in the unconstrained (inactive)
conformation. By contrast, binding mode 4 was clearly
disfavored in chain A, indicating that this binding
mode is incompatible with the partially constrained
(active) conformation. The ERα agonist fluoligand assay
was performed by Cerep (Paris, France) using YC-1 final
concentrations from 10 to 250 μM.
Gene expression analyses
RNA samples were extracted using TRIzol reagent (Life
Technologies, Carlsbad, CA, USA) and the RNeasy kit
(QIAGEN, Valencia, CA, USA), and quality was evaluated
in the Agilent 2100 Bioanalyzer (Agilent Technologies,
Santa Clara, CA, USA). RNAs were amplified using the
Ribo-SPIA system (NuGEN Technologies, San Carlos,
CA, USA) and subsequently hybridized on the Human
Genome U219 microarray platform (Affymetrix, Santa
Clara, CA, USA). The data have been deposited in the
Gene Expression Omnibus (GEO) [GSE:38829]. Publicly
available whole-genome expression data for 51 breast cancer cell lines were analyzed using the preprocessed and
normalized values [18]. The Gene Set Expression Analysis
(GSEA) was run using default values for all parameters
[24]. Preprocessed and normalized microarray data from
Page 3 of 16
breast tumors and tumor response to tamoxifen were
taken from the corresponding repositories: the Stanford
microarray repository (NKI-295 data set) [25] and the
GEO record [GSE:9195], respectively. Cox proportional
hazard regression analysis was used to evaluate differences
in distant metastasis-free survival according to VAV3
expression (three microarray probes were treated
Chromatin immunoprecipitation data analysis
Chromatin immunoprecipitation (ChIP) data were downloaded from the GEO database [GSE:32222] [7] and
analyzed using MACS version 2.0.9 software (macs2diff
function) [26]. Significance was defined by a Q-value <0.01
and using default values for the remaining parameters.
Differentially bound genomic regions were annotated to
the closest ENSEMBL (hg19) annotated gene using the
R-Bioconductor package ChIPpeakAnno [27]. Previously
aligned reads were extracted from the sequence read archive [SRP:032421], and sequence counts were normalized
to the library size. ERα and nonspecific immunoglobulin
control (IgG) ChIP assays were performed as previously
described [28,29]. Briefly, the DNA was purified using a
phenol-chloroform extraction protocol, the antibodies
used were anti-ERα (SC-543 and SC-7207; Santa
Cruz Biotechnology) and anti-IgG (ab46540; Abcam,
Cambridge, UK), and three independent biological
replicates were obtained in all cases. The primers used
were site 1: forward 5′-CACTTCCTTTCCTGGTTGGA3′ and reverse 5′-AGTAAAAGGGGTGCCCTCTC-3′,
and site 2: forward 5′- TGTGGTGTTTCCTGTTAGT
Antibodies and RAC1 activity assay
The antibodies we used were anti-E2F1 (KH95; Santa
Cruz Biotechnologies), anti–epidermal growth factor
(anti-EGFR) (1005; Santa Cruz Biotechnologies), antiERα (SP-1; Abcam), antibody against phosphorylated
extracellular signal-regulated protein kinases 1 and 2
(anti-phospho-ERK1/2) (D13.14.4E; Cell Signaling Technology, Danvers, MA, USA), anti-NUP62 (nucleoporin
62 kDa, clone 53; BD Transduction Laboratories, San Jose,
CA, USA), anti-PAK1 (2602; Cell Signaling Technology),
anti-RAC1 (05-389; EMD Millipore), anti-phospho-serine
235/236 ribosomal S6 (D57.2.2E; Cell Signaling Technology), anti-VAV3 (07-464, Millipore; and 2398, Cell Signaling
Technology), anti-phospho-tyrosine 173 VAV3 (anti-pT173
VAV3, ab52938; Abcam) and anti–tubulin α (anti-TUBA)
(DM1A + DM1B; Abcam). Secondary antibodies for used
for immunofluorescence (Alexa Fluor) were obtained from
Molecular Probes (Eugene, OR, USA). To measure RAC1
activity, we used the Rac1 G-LISA Activation Assay
Biochem Kit (BK128; Cytoskeleton, Denver, CO, USA).
Aguilar et al. Breast Cancer Research 2014, 16:R53
The MYC-Vav3 wild-type and oncogenic expression constructs we used have been described previously [30,31].
Short hairpin RNA assays
For the ESR1 and VAV3 expression depletion assays, we
used MISSION shRNA (Sigma-Aldrich). The lentiviral
packaging, envelope, control and green fluorescent protein
(GFP) expression plasmids (psPAX2, pMD2.G, non-hairpin-pLKO.1, scrambled-pLKO.1 and pWPT-GFP) were
purchased from Addgene (Cambridge, MA, USA). Production and collection of lentiviral particles were carried
out according to a modified Addgene protocol. Initial viral
titers >5 × 105/ml were confirmed by Lenti-X GoStix lentivirus testing (Clontech Laboratories, Mountain View, CA,
USA), and supernatants were then concentrated by ultracentrifugation or with the Lenti-X Concentrator (Clontech
Laboratories) and stored at −80°C. Concentrated viral
supernatants were titrated for optimal inhibition of
the target.
Genetic association study
The Institutional Review Board of the Institute of Medical
Science (The University of Tokyo) approved the study,
and written informed consent was obtained from all
patients. A total of 240 patients with primary breast
cancer, recruited by the Shikoku-*10 collaborative group
(Tokushima Breast Care Clinic, Yamakawa Breast Clinic,
Shikoku Cancer Center, Kochi University Hospital and
Itoh Surgery and Breast Clinic), Kansai Rosai Hospital,
Sapporo Breast Surgical Clinic and Sapporo Medical
University Hospital from September 2007 to September
2008, were included in the genome-wide association study
(GWAS), and 105 patients recruited by the same centers
from October 2008 to January 2010 were included in the
replication study. All patients were Japanese women
pathologically diagnosed with ERα-positive invasive breast
cancer. They received adjuvant tamoxifen monotherapy
between 1986 and 2008. The data on primary breast cancer diagnoses or recurrences were confirmed by extraction
from the patients’ medical records. Patients without
recurrence were censored at the date of the last clinical
evaluation. Recurrence-free survival time was defined as
the time from surgical treatment to the diagnosis of breast
cancer recurrence (locoregional, distant metastasis or
contralateral breast events) or death. Patients received
tamoxifen 20 mg/day for 5 years. Treatment was stopped
at the time of recurrence. Genomic DNA was extracted
from peripheral blood or frozen breast tissue using the
QIAGEN DNA Extraction Kit. In the GWAS, 240 patients
were genotyped using the Illumina Human610-Quad
BeadChip array (Illumina, San Diego, CA, USA). Quality
control was assured by excluding single-nucleotide polymorphisms (SNPs) with low call rates (<99%) and those
with a Hardy–Weinberg equilibrium P-value <1.0 × 10−6.
Page 4 of 16
SNPs with a minor allele frequency <0.01 were also
excluded from the analyses. The multiplex PCR-based
Invader assay (Third Wave Technologies, Madison, WI,
USA) on ABI PRISM 7900HT (Applied Biosystems, Foster
City, CA, USA) was used in the replication study. For statistical analysis, recurrence-free survival curves were estimated using the Kaplan–Meier method. The statistical
significance of relationships between clinical outcomes
and genetic variations was assessed using a logrank test.
Tumor series and immunohistochemistry
For the Bellvitge Institute for Biomedical Research
(IDIBELL, Barcelona, Spain) cohort, the IDIBELL Ethics
Committee approved the study and written informed consent was obtained from all patients. Twenty-nine patients
treated with primary endocrine therapy before surgical
excision of breast tumors were chosen from the clinical
database activity of the Catalan Institute of Oncology
(ICO) Breast Cancer Unit. All patients were postmenopausal and diagnosed with ERα -positive and HER2negative breast cancer. The patients received treatment
with either an ERα antagonist (tamoxifen or toremifene)
or an aromatase inhibitor (letrozole or exemestane). Patients received therapy until a maximum response was
achieved (range, 4 to 27 months), unless tumor progression was observed during a twice-monthly radiological
and clinical assessment. After endocrine therapy was completed, full tumor excision was performed by either lumpectomy or radical mastectomy. Response was defined as
the percentage of fibrosis and other patterns of pathological response attributable to tumor reduction at surgery. Tissue was obtained at surgery or biopsy, fixed in
buffered formalin and processed for use in paraffinembedded sections. A Stockholm cohort was analyzed in
the Swedish study, which consisted of postmenopausal
breast cancer patients enrolled in a randomized adjuvant
trial between November 1976 and April 1990. The study
design and long-term follow-up data were previously
reported in detail [32]. Ethical approval for the Swedish
study was obtained from the Karolinska Institute Ethics
Council. Immunohistochemistry was performed using the
heat-mediated antigen retrieval method with citrate buffer.
The VAV3 polyclonal antibody used for immunohistochemistry has been described previously [30]. Scoring of
the immunohistochemical results was performed in a
blind and independent manner by two pathologists.
A chemical compound screen identifies YC-1 as reducing
viability of cellular models of acquired resistance
Acquired resistance to aromatase inhibitors in postmenopausal women can be modeled in MCF7-LTED cells [17].
Using this model, we carried out a cell-based chemical
compound screen out to identify potential therapeutic
Aguilar et al. Breast Cancer Research 2014, 16:R53
strategies that could prevent and/or overcome resistance.
More than 1,200 compounds were assessed for their
differential effect on the viability of MCF7-LTED cells (as
defined by MTT-based assays) relative to the parental
MCF7 cells. Thirteen compounds showed higher relative
inhibition in MCF7-LTED cells (Figure 1A and Additional
file 1: Table S1). Subsequent validation using independent
cell cultures and compound solutions identified YC-1
(3-(5′-hydroxymethyl-2′-furyl)-1-benzylindazole) as being
the most effective, with a 27-fold difference in the
half-maximal inhibitory concentration (IC50) was revealed between MCF7-LTED and MCF7 cells (4.9 μM and
131 μM, respectively) (Figure 1B).
YC-1 is a direct activator of soluble guanylyl cyclase
(sGC) Thus, increased levels of cGMP were observed in
cell cultures exposed to this compound (Figure 1C).
Next, the effect of YC-1 on a collection of breast cancer
cell lines was examined. IC50 values <10 μM were obtained for several cell lines (Additional file 2: Table S2),
including MCF7-LCC9 and MCF7-LY2, which correspond to models of acquired resistance to fulvestrant and
Page 5 of 16
to the raloxifene analogue LY-117018, respectively.
These cell lines also showed cross-resistance to tamoxifen [33,34].
Intriguingly, an activator of sGC derived from the
structural development of YC-1, BAY 41-2272, displayed
a lower differential inhibitory effect (Additional file 3:
Figure S1A). In addition, assessment of another sGC
activator, A-350619, and complementary evaluation of
an inhibitor of phosphodiesterase activity did not reveal
the expected differences (Additional file 3: Figure S1B).
Although YC-1 has been used extensively in cancer
research, including preclinical studies in breast cancer
[35], it is unclear whether a direct target beyond sGC
YC-1 binds to estrogen receptor α
To investigate novel molecular targets of YC-1, the chemical structure of YC-1 was used to query the ChEMBL
[36] and BindingDB [37] databases for similar compounds
with reported biological activity. Strikingly, WAY-169916,
which has been shown to bind ERα [38], and a series of
Figure 1 A chemical compound screen identifies an activator of soluble guanylyl cyclase as reducing the viability of long-term
estrogen-deprived MCF7 cells. (A) Compounds with no differential effect (top panel), with an inhibitory effect on the long-term estrogen
deprivation (LTED) of MCF7 cells (MCF7-LTED cells) relative to MCF7 cells (middle panel) and with an inhibitory effect on MCF7 relative to
MCF7-LTED cells (bottom panel). The y-axis indicates relative viability of MCF7-LTED cells, and the x-axis indicates increasing concentrations of the
compounds. Colored lines indicate average values. (B) Corroboration of the inhibitory effect of YC-1 on MCF7-LTED cells. (C) Time-dependent
increase of cGMP in MCF-LTED cells exposed to YC-1.
Aguilar et al. Breast Cancer Research 2014, 16:R53
related compounds [23,39] were retrieved at a 60% similarity cutoff value. WAY-169916 is an unusual ERα ligand:
It is able to bind ERα, leading to its constrained or unconstrained conformation (responsible for partial agonist
activity, binding mode 1, or for an antagonist effect, binding mode 4, respectively) [23]. The relative preferences for
these ERα conformations explain the graded activities
across the compound series [23]. Thus, compound 6
(hereinafter referred to as WAY6) was the WAY-169916
analogue most similar to YC-1 (Figure 2A), which was
found to lead preferentially to the unconstrained conformation [23].
Molecular docking was used to examine the potential
binding mode of YC-1 to ERα. The predicted mode was
very similar to binding mode 1 of WAY6 when docked
in both the partially constrained (Figure 2B) and unconstrained (Additional file 4: Figure S2A) conformations.
Although a binding mode similar to binding mode 4 was
also found to be possible in the latter conformation
(Additional file 4: Figure S2B), it had a lower score. As
shown in Figure 2B, the binding mode of YC-1 was
almost perfectly aligned with WAY6 and maintained
the main molecular interactions with ERα, which
comprised van der Waals contacts with the lipophilic
cavity and a double hydrogen bond with Glu353 and
Arg394. The absence of the trifluoromethyl group,
which is engaged in a weak hydrogen bond with His524,
could cause some loss of potency, but this group was not
essential for the biological activity in the WAY-169916
series [38].
Page 6 of 16
The MCF7-LTED model was previously shown to be
less sensitive to fulvestrant than the parental MCF7 [17],
and this difference appeared to be coherent with the
described differential ERα binding mode of fulvestrant
relative to WAY-169916 [23]. Next, to validate the binding prediction between YC-1 and ERα, we performed an
agonist fluoligand assay, which showed the competition
with fluorescein-labeled estradiol. The results of this
assay revealed YC-1 IC50 and Ki values of 33 μM and
26 μM, respectively (Figure 2C), which are in agreement
with the inhibitory effects observed in the cell lines
(Additional file 2: Table S2). Intriguingly, two of the cell
lines that showed relative inhibition by YC-1 (AU565
and SKBR3) are generally considered ERα-negative [18].
Thus, the combined targeting of at least sGC and ERα
would make it difficult to interpret the phenotypic consequences of therapy based on YC-1. Consequently, the
specific molecular perturbations mediated by YC-1
should be identified.
Molecular perturbations mediated by YC-1
Having defined breast cancer cell lines with relatively
higher sensitivity to YC-1, we evaluated the existence of
a common molecular signature among these lines. The
GSEA [24] tool was used to examine gene set expression
differences between cell lines of “high” and “low” sensitivity (defined by an IC50 threshold of 10 μM) (Additional
file 2: Table S2). The cell lines with higher sensitivity
to YC-1 had overexpression of cell cycle pathway genes,
whereas the less sensitive cell lines cells showed
Figure 2 YC-1 binds to estrogen receptor α. (A) Chemical structures of WAY-169916, WAY6 and YC-1. (B) Predicted binding mode of YC-1
(purple) in the partially constrained conformation of estrogen receptor α (ERα) (chain A, Protein Data Bank code 3OS8 [Swiss-Prot:P03372]). The
binding mode of WAY6 (white sticks) is shown as a reference. (C) The results of the ERα agonist fluoligand assay using YC-1 are shown, along
with the concentration–inhibition curve with duplicates. *YC-1 was not completely soluble at concentrations >100 μM.
Aguilar et al. Breast Cancer Research 2014, 16:R53
overexpression of ribosome pathway genes, among
others (Additional file 5: Figure S3 and Additional file 6:
Table S3). These results are consistent with the increased
dependence of the cell cycle and proliferation highlighted
in endocrine therapy resistance in previous studies [40].
To examine the potentially selective YC-1 mechanism
of action in models of acquired resistance, the levels and
subcellular localization of ERα were examined. Although
both were altered by YC-1 treatment, no substantial
differences were observed between MCF7 and MCF7LTED cells (Additional file 7: Figure S4). Subsequently,
whole-genome expression data were obtained for both
cell lines in basal and YC-1 exposure conditions. Consistent with the results described above, expression of
the ribosome pathway was clearly differentiated between
MCF7 and MCF7-LTED cells in basal conditions and
with exposure to YC-1 (Additional file 8: Figures S5A
and S5B and Additional file 9: Table S4). Exposure to YC-1
led to a significant alteration of the cell cycle pathway in
both settings (Additional file 8: Figure S5B). Accordingly,
targets of a central positive regulator of the cell cycle,
E2F1, were revealed to be significantly underexpressed with
exposure to YC-1 (Additional file 10: Table S5). Protein
analysis revealed a larger relative decrease in the expression
of this transcription factor in MCF7-LTED cells exposed to
YC-1 (Additional file 8: Figure S5C). Together, these results
indicate that YC-1 may reduce the potential of cell proliferation in such a way that MCF7-LTED cells are relatively
more sensitive.
Having observed pathway differences, we aimed to
identify the largest gene expression differences between
MCF7 and MCF7-LTED cells exposed to YC-1. Thus,
we defined a twofold or greater change in MCF7-LTED
cells (between basal and YC-1 conditions), and a 1.5-fold
or greater expression change in MCF7 cells. In this analysis, we identified 19 and 8 genes, respectively, that were
down- and upregulated in MCF7-LTED cells exposed to
YC-1 (Figure 3A). Consistent with the binding of YC-1 to
ERα, many of these perturbed genes corresponded to loci
that are differentially regulated by ERα in endocrine
therapy resistance. Analysis of ChIP data of responsive
and nonresponsive breast tumors [7] revealed significant
differential ERα binding at several of these loci, with 10 of
27 showing increased binding in the nonresponsive setting
(Figure 3B). From among this set, VAV3 was further
included in a 271-gene list associated with poor clinical
outcome [7]. Following on from these observations, we
performed ERα ChIP assays using extracts of MCF7 and
MCF7-LTED cells in basal (DMSO) or YC-1-exposed
conditions. By this method, we found two VAV3 sites with
significant binding of ERα relative to the nonspecific immunoglobulin control (Figure 3C). In addition, both sites
showed ERα sensitivity (that is, lower binding) with exposure to YC-1, and one site (binding site 1) had significantly
Page 7 of 16
more binding (2.4-fold) in MCF7-LTED cells than in
MCF7 cells (Figure 3C). Similarly, specific analysis of these
sites in the original breast cancer data set [7] showed substantial ERα binding in nonresponder and metastasis cases
(Figure 3D). Consistent with these observations, and
among the potential ERα downstream effectors identified
above, VAV3 showed the highest expression associated
with ESR1 in breast tumors [25] (mutual information =
0.23, P < 0.001). Moreover, shRNA-mediated depletion of
ERα revealed a decrease of VAV3 in MCF7-LTED cells,
but not in parental MCF7 cells (Figure 3E). Collectively,
these results indicate that VAV3 function may be critical
in endocrine therapy resistance governed by ERα transcriptional regulatory plasticity.
VAV3 is perturbed by YC-1 and determines acquired
Consistent with the observations described above, total
and pY173 VAV3 (whose phosphorylation regulates activity) [31,41] decreased in MCF7-LTED cells, but not in
MCF7 cells, exposed to YC-1 (Figure 4A). According to
the position of VAV3 in its canonical signaling pathway,
EGFR levels were decreased in both MCF7 and MCF7LTED cells exposed to YC-1, but ERK1/2 phosphorylation
was decreased only in MCF7-LTED cells exposed to YC-1
(Figure 4A). In addition, PAK1 and RAC1 levels were not
altered under these conditions (Figure 4A). Similarly to
MCF7-LTED, MCF7-LCC9 cells exposed to YC-1 showed
loss of expression of VAV3, but not of PAK1 or RAC1
(Figure 4B). Nonetheless, depletion of VAV3 reduced
RAC1 activity in both MCF7 and MCF7-LTED cells,
and this alteration was recovered through reconstitution
using a shRNA-resistant MYC-Vav3 expression construct
(Additional file 11: Figure S6).
Next, lentivirus-mediated transduction of shRNAs
directed against expression of VAV3 significantly reduced
the viability of MCF7-LTED and MCF7-LCC9 cells relative to MCF7 cells (P < 0.05) (Figure 4C). A clonogenic
assay also indicated relative loss of viability of MCF7LTED cells, and, to a lesser extent, MCF7-LCC9 cells, with
shRNA-mediated depletion of VAV3. Differences relative
to MCF7 cells were <0.8-fold (Figure 4D). Reconstitution
with MYC-Vav3 significantly recovered proliferation in
MCF7-LTED cells, although not to the level of the shRNA
control assay (Figure 4E), which might have been due to
Vav3 overexpression (Additional file 11: Figure S6) and/or
to specific roles of splicing variants. Reconstitution with
Vav1 or Vav2 could not be assessed, as the overexpression
of the murine counterparts caused cell death (data not
shown). Analysis of poly(ADP-ribose) cleavage did not
reveal substantial differences among the cell lines
(Figure 4F), which further indicates that YC-1 primarily
inhibits cell proliferation. Thus, a reduction in E2F1 was
observed in MCF7-LTED cells exposed to 2 μM YC-1
Aguilar et al. Breast Cancer Research 2014, 16:R53
Page 8 of 16
Figure 3 Genes specifically perturbed by YC-1 in long-term estrogen deprivation of MC7 cells and their link to the response to endocrine
therapy. (A) Genes whose expression change differentiates the effect of YC-1 between long-term estrogen deprivation of MCF7 (MCF7-LTED) cells
and MCF7 cells. The bottom heatmap shows the normalized expression differences for the probes and genes that passed the defined thresholds.
DMSO, Dimethyl sulfoxide. (B) Logarithmic fold changes between the responder and nonresponder breast tumors for the genes (Gene Expression
Omnibus data set [GSE:32222]) shown in (A) determined by chromatin immunoprecipitation (ChIP) assay. (C) ChIP assay results for estrogen receptor α
(ERα) and immunoglobulin G (IgG) at two sites in the VAV3 locus, both for MCF7 and MCF7-LTED cells with or without exposure to YC-1 (significant
differences are indicated by asterisks: *P < 0.05, **P < 0.01, ***P < 0.001). The bottom graph shows the genomic locus with the linkage disequilibrium
structure in Japanese individuals found in HapMap and the relative position of the variant rs10494071 (presented below). (D) Detailed
analysis of the Gene Expression Omnibus [GSE:32222] data set for the two sites depicted above. Left panels show the normalized average
intensity of ERα binding ±500 bp around the sites in different sample sets as depicted in the insets. Middle panels show relative ERα binding in the
above sites across 23 breast cancer samples. Right panels are graphs showing the number of cases with or without an ERα binding event (peak) in
nonresponders and responders. Top right panels show that 44% of the nonresponders had ERα binding, whereas only two (22%) of nine responders
had binding. Bottom right panels show that 78% of the nonresponders had ERα binding, whereas only one (11%) of nine responders had binding.
(E) Short hairpin RNA (shRNA)–mediated depletion of ERα led to a decrease in VAV3 levels in MCF7-LTED cells, but not in MCF7 cells.
Aguilar et al. Breast Cancer Research 2014, 16:R53
Page 9 of 16
Figure 4 Study of VAV3 in models of acquired resistance to endocrine therapies. (A) Western blot analysis results for VAV3 (pT173 and
total, top panels), signaling components and control tubulin α (TUBA) from MCF7 cells and long-term estrogen deprivation of MCF7 (MCF7-LTED)
cells in basal and YC-1 exposure conditions. pERK, phosphorylated extracellular signal-regulated protein kinase. (B) Western blot analysis results
for VAV3, PAK1 and RAC1, as well as control TUBA, in MCF7-LCC9 cell extracts from basal and YC-1 exposure conditions. (C) Short hairpin RNA
(shRNA)–mediated depletion of VAV3 reduces the viability, in methylthiazol tetrazolium (MTT)–based assays) of MCF-LTED and MCF7-LCC9 cells
relative to parental MCF7. The asterisks correspond to significant differences (P < 0.05) in the viability rate (slope of the trends (shown), including
three replicas, and relative to the control pLKO.1). The bottom right panel shows the results of short hairpin RNA (shRNA)–mediated depletion of
VAV3 relative to the negative control assay. (D) shRNA-mediated depletion of VAV3 reduces the viability (clonogenic assays) of MCF-LTED and
MCF7-LCC9 cells. (E) Reconstitution with MYC-Vav3 partially recovers viability of MCF-LTED cells. The asterisk corresponds to a significant
difference (P < 0.05) relative to shRNA-mediated depletion of VAV3. (F) No substantial differences in poly(ADP-ribose) (PARP) cleavage were
observed between MCF7 and MCF7-LTED cells exposed to YC-1. (G) Top panel, reduction of E2F1 expression in MCF7 and MCF7-LTED cells
exposed to YC-1; MCF7-LTED, but not the parental MCF7, show a reduction at 2 μM YC-1. Bottom panel, control TUBA.
(Figure 4G). These results are also consistent with those
of VAV3 depletion in prostate cancer cells [42].
VAV3/VAV3 association with clinical outcome
Having identified VAV3 as a determinant of acquired
resistance in cellular models, we next assessed its relevance in the clinical scenario. By examining the results of
a Japanese GWAS regarding response to tamoxifen [43],
we identified 20 SNPs in VAV3 that are associated with
clinical outcomes (logrank P-values <0.05) (Additional
file 12: Table S6). In a subsequent assessment of an independent patient series, the associations in several SNPs
were replicated. Of the variants analyzed, rs10494071
showed the strongest association in the combined analysis
(P = 8.4 × 10−4) (Figure 5A). The rs10494071 variant is
located within VAV3 intron 19 (Figure 3C) and may represent an expression quantitative trait locus. In a study of
monocytes [44], the minor allele was associated with lower
expression levels of VAV3 (P = 2.2 × 10−11).
An association between the rs10494071 minor allele,
which in turn was associated with a better tamoxifen
response (Figure 5A), and lower germline expression of
VAV3 seemed to be consistent with mediation of resistance by this signaling component. Next, we analyzed an
expression data set from ERα-positive breast cancer
patients treated with tamoxifen [45]. The results of this
Aguilar et al. Breast Cancer Research 2014, 16:R53
Page 10 of 16
Figure 5 Association between VAV3/VAV3 and clinical response to endocrine therapies. (A) Association between rs10494071 and the
response to tamoxifen in Japanese patients. The Kaplan–Meier curves show the recurrence-free survival rate over time (years) and between
patients stratified according to the three possible rs10494071 genotypes (TT, TC and CC). The logrank test P-values are shown. (B) Association
between VAV3 tumor expression and response to tamoxifen (Gene Expression Omnibus data set [GSE:9195]). Graphs show the proportion of
patients with metastasis-free survival over time (years) and stratified according to high (above the median) or low (below the median) VAV3
expression in breast tumors. The results shown are for three VAV3 microarray probes. Logrank P-values are shown. (C) Association between VAV3
tumor expression and the pathological response to endocrine therapies. Top panel: Graph depicting the correlation between VAV3 immunostaining
score and pathological response (percentage of tumor reduction posttreatment). Bottom panels: Representative examples of the three immunostaining
scores. Insets: Cells with nuclear and cytoplasmic positivity. PCC, Pearson’s correlation coefficient. (D) Examples of increased VAV3 staining at the invasive
tumor front. (E) Graphs showing the Kaplan–Meier curves for patients who did or did not receive tamoxifen in the Swedish study. Panels from left to
right show the results for patients whose tumors revealed low, medium or high nuclear VAV staining. The logrank test P-values are shown.
Aguilar et al. Breast Cancer Research 2014, 16:R53
Page 11 of 16
analysis also suggest that low VAV3 expression might be
associated with better outcomes (logrank P-values <0.05
for two probes) (Figure 5B).
Complementarily to the germline association study, we
assessed a series of 29 breast tumors, which had been
collected by biopsy after endocrine therapy, for VAV3 expression by immunohistochemistry. A negative correlation
(Pearson’s correlation coefficient = −0.51, P = 0.006) was
revealed between the scores of VAV3 staining (low, medium or high) and the pathological response to therapy
(that is, tumor reduction) (Figure 5C). These 29 cases
included a variety of endocrine therapies, but no bias with
respect to therapy type was apparent. Moreover, consistent with the role of VAV3 in promoting breast cancer progression [30], comparatively higher staining was observed
at the tumor fronts (Figure 5D). In addition, higher staining scores could be linked to nuclear positivity (insets in
Figures 5C and 5D), and, intriguingly, this localization has
previously been shown to be necessary for the function of
the androgen receptor in prostate cancer [46].
To further assess the above-described immunohistochemical association, we performed an independent
tumor tissue microarray analysis with detailed molecular,
histopathological and clinical information [32,47-49].
The results of this study revealed a significant association between the benefit of tamoxifen therapy and low
nuclear VAV3 staining. Conversely, high nuclear VAV3
was not associated with tamoxifen benefit (Figure 5E). In
addition, nuclear VAV3 was found to be positively correlated with markers of poor therapy response, particularly
phospho-Ser305 ERα and nuclear phospho-Ser473 AKT
(P-values <0.01) (Table 1) [48]. These correlations, and
those between cytoplasmic VAV3 and tumor size and
grade, as well as ERα/PR status, were analogous to those
previously observed for nuclear and cytoplasmic PAK1
[49] (Table 1). The interpretation of the negative and
positive correlations, respectively, of phospho-Ser65 4EBP1
and nuclear S6K2 [47] with nuclear VAV3 may be more
complex; indeed, we observed a modest correlation between cytoplasmic VAV3 and phospho-Ser2448 mammalian target of rapamycin (mTOR) (P = 0.034). Together,
these data reinforce the link between the VAV3 signaling
axis and resistance to endocrine therapy.
Therapeutic strategy based on VAV3 evidence
Therapy based on YC-1 should be discouraged because of
its multiple targets. In addition, to date, no compounds
that specifically target VAV proteins have been identified.
Having identified a critical role for VAV3, we hypothesized
that compounds whose IC50 value is inversely correlated
with VAV3 expression might represent promising therapeutic strategies for the endocrine therapy–resistant
setting. To test this hypothesis, we analyzed data from the
Genomics of Drug Sensitivity in Cancer project [50]. In
this analysis, we found that the strongest positive and
negative IC50 correlations with VAV3 expression across all
cancer cell lines were for thapsigargin and erlotinib, respectively (Figure 6A). These correlations appeared robust
in the analysis of breast cancer only (Figure 6A, insets).
Notably, the finding that VAV3 expression opposes the
effect of thapsigargin is congruent with those of previous
studies of VAV proteins [51,52]. Conversely, erlotinib
inhibits EGFR, which has been extensively linked to endocrine therapy resistance [1,53]. Importantly, VAV3 functions downstream of receptor protein tyrosine kinases,
which include EGFR [54]. In accordance with these
Table 1 VAV3 nuclear and cytoplasmic expression in relation to other tumor markers assessed by the Spearman’s rank
Nuclear VAV3, n (%)
All tumors
Cytoplasmic VAV3, n (%)
607 (85.9)
3 (0.4)
43 (6.1)
54 (7.6)
229 (32.4)
154 (21.8)
215 (30.4)
109 (15.4)
Tumor size (>20 mm vs. ≤20 mm)
Rs = −0.04, P = 0.30
Rs = 0.13, P = 0.0009
Tumor grade (1, 2 or 3)
Rs = −0.09, P = 0.026
Rs = 0.16, P = 0.00007
ERα (>10% vs. ≤10%)
Rs = 0.05, P = 0.20
Rs = −0.12, P = 0.002
PR (>10% vs. ≤10%)
Rs = 0.06, P = 0.14
Rs = −0.15, P = 0.0002
HER2 status (positive vs. negative)
Rs = 0.00, P = 0.99
Rs = 0.05, P = 0.16
Phospho-Ser167 ERα (%)
Rs = 0.12, P = 0.002
Rs = −0.11, P = 0.003
Phospho-Ser305 ERα (%)
Rs = 0.11, P = 0.006
Rs = −0.09, P = 0.016
PAK1 (cytoplasm 0 to 3 positivity)
Rs = −0.07, P = 0.077
Rs = 0.12, P = 0.003
Phospho-Ser473 AKT (nuclear %)
Rs = 0.18, P < 0.00001
Rs = −0.20, P < 0.00001
Rs = 0.06, P = 0.11
Rs = −0.08, P = 0.034
Phospho-Ser2448 mTOR (high vs. low)
Phospho-Ser65 4EBP1 (cytoplasm 0 to 2 positivity)
Rs = −0.15, P = 0.0001
Rs = 0.19, P < 0.00001
S6K2 (nuclear %)
Rs = 0.21, P < 0.00001
Rs = −0.26, P < 0.00001
ERα, Estrogen receptor α; mTOR, mammalian target of rapamycin; PR, Progesterone receptor. P < 0.05 values are statistically significant.
Aguilar et al. Breast Cancer Research 2014, 16:R53
Page 12 of 16
Figure 6 Correlation analysis between VAV3 expression and compounds: half-maximal inhibitory concentration identifies erlotinib as a
potential therapeutic compound. (A) Graph showing the correlation between VAV3 expression (two probes showed similar results, depicted for
218807_s_at) and erlotinib (left panel) or thapsigargin (right panel) logarithmic half-maximal inhibitory concentration (IC50) values across all cancer
cell lines. Spearman’s correlation coefficient (SCC) and the corresponding P-values are shown. Red lines indicate trends, and insets show results
for breast cancer cell lines only. (B) Graph showing the inhibitory effect of erlotinib on long-term estrogen-deprived MCF7 (MCF7-LTED) cells
relative to parental MCF7 cells. (C) Top panels, Western blot analysis results for VAV3 (total), pT173 VAV3 and control tubulin α (TUBA) from MCF7
and MCF7-LTED cells in basal and erlotinib exposure conditions. Bottom panels, Western blot analysis results for pT173 VAV3 and control TUBA
from MCF7 and MCF7-LTED cells with or without epidermal growth factor (EGF).
observations, exposure to erlotinib significantly reduced
the viability of MCF7-LTED relative to MCF7 cells
(Figure 6B). VAV3 expression was not reduced by exposure to erlotinib (contrary to exposure to YC-1), but we observed a partial reduction in pY173 VAV3 in MCF7-LTED
cells (Figure 6C, top panels). Accordingly, exposure to
EGF increased pY173 VAV3 in this setting (Figure 6C,
bottom panels). Collectively, these results further endorse
a critical role for VAV3 in endocrine therapy resistance.
The results of this study suggest that VAV3 function
mediates the response to endocrine therapies in breast
cancer and, as a result, the acquisition of resistance. In this
context, VAV3 might be a key effector whose expression is
differentially regulated by ERα [7]. Thus, the expression
regulation of VAV3 would be relatively more dependent
on ERα in the endocrine therapy–resistant setting. Conversely, in previous studies, researchers have proposed
that VAV3 is an activator of ERα [55,56]. These observations could indicate the existence of a feedback mechanism that would ultimately regulate growth factor signaling.
Indeed, VAV3 has been shown to activate receptor protein
tyrosine kinases and RAC1 [54-56], and an inhibitor of
this protein can decrease both estrogen-induced cell proliferation and MCF7-tamoxifen-resistant cell growth [56].
Notably, authors of an independent report identified
VAV3 as a marker for posttreatment recurrence of prostate cancer [57]. Together with our analysis of VAV3 in
breast tumors, these observations further endorse the link
Aguilar et al. Breast Cancer Research 2014, 16:R53
between the VAV3-RAC1-PAK1 signaling axis and resistance to endocrine therapies. Nevertheless, analysis of
differential gene expression by exposure to YC-1 may
point to complementary mediators of endocrine therapy
resistance. Activation of ERBB4 has previously been linked
to this setting [58-60], and two other identified perturbations (GLI3 and PTCH1) belong to the Hedgehog signaling pathway, which has been highlighted as a possible
therapeutic target in this setting [61]. Whether these proteins act functionally in concert with VAV3 or whether
they represent necessary alterations in different biological
processes or pathways remains to be determined.
The association between genetic variation in VAV3 and
the response to tamoxifen could allow the stratification of
patients according to potential clinical benefit. However,
this association should be replicated in independent studies with larger samples. The rs10494071 minor allele has a
relatively high frequency in the Japanese population, but is
rare in individuals of European ancestry (45% and 5%,
respectively, according to HapMap data). This is also
the case with a variant in linkage disequilibrium with
rs10494071 (data not shown). These observations indicate
that an attempt to replicate the association in a nonJapanese population will require dense genotyping at the
specific locus.
Although the results of the genetic association should be
replicated, they are consistent with the anticipated functional role of VAV3 and with the observations made in gene
expression analyses. In our present study, we identified an
association between the rs10494071 minor allele and better
tamoxifen response, and, in turn, we found in our analysis
of a tumor data set that low VAV3 expression correlates
with better tamoxifen response [45]. Additionally, these
observations seem to be coherent with the role of the
rs10494071 variant as an expression quantitative trait locus
for VAV3, with the minor allele being associated with
significantly lower gene expression in monocytes [44].
Importantly, in a previous study in which the researchers
identified VAV3 as a marker for posttreatment recurrence
of prostate cancer, the association was in the same direction
[57]. Moreover, these results are consistent with, and the
conclusions further endorsed by, the associations revealed
for nuclear VAV3 and tamoxifen therapy response, as well
as the observed correlations between the expression of
VAV3 and known tumor markers linked to therapy response. However, further work is required to elucidate the
functional difference between nuclear and cytoplasmic
VAV3, which is reminiscent of the results for PAK1 [49]
and could be linked to the activation of the androgen receptor, as previously shown in prostate cancer [46,62].
It has been firmly established that growth factor signaling influences the response to endocrine therapies and,
consequently, the acquisition of resistance. Among other
evidence, overexpression of growth factor receptors,
Page 13 of 16
including EGFR, has been associated with decreased sensitivity to endocrine therapy and poorer prognosis [63].
Akin to this observation, other researchers have reported
that cell models of endocrine therapy resistance overexpress several growth factor receptors, also including EGFR
[17]. In turn, these observations have led to the design of
clinical trials to assess the target inhibition of the receptors [64]. In this scenario, the analysis of VAV3 expression
and/or function could potentially help to identify patients
that may benefit from therapies aimed at preventing and/
or overcoming endocrine therapy resistance.
In this study, we have identified VAV3 as a critical mediator of endocrine therapy resistance in breast cancer
downstream of ERα and growth factor receptor signaling.
The expression of VAV3 may be specifically regulated by
ERα in the endocrine therapy–resistant setting. The results of our genetic and immunohistochemical studies indicate that VAV3/VAV3 represents a promising biomarker
for predicting the response to endocrine therapies. Despite
the lack of targeted therapies for VAV proteins, inhibition
of EGFR signaling could potentially prevent and/or overcome endocrine therapy resistance mediated by VAV3.
Additional files
Additional file 1: Table S1. Results from the chemical compound
Additional file 2: Table S2. Values of YC-1 IC50 (μM) in breast cancer
cell lines.
Additional file 3: Figure S1. Assessment of the activation of sGC in the
viability inhibition of MCF7-LTED cells. (A) BAY 41-2272 shows an effect,
but less than that of YC-1. (B) A-350619 (activator of sGC) and sulindac
sulfide (inhibitor of phosphodiesterase) do not show the predicted effects
in MCF7-LTED cells. In fact, the contrary is observed; A-350619 appears to
be more effective in MCF7 cells.
Additional file 4: Figure S2. Study of the binding mode of YC-1 to
ERα. (A) Predicted binding mode of YC-1 (purple) in the unconstrained
conformation of ERα (chain C, PDB code 3OS8). The binding mode of
WAY6 (white sticks) is shown as reference. (B) Docking pose of YC-1
(purple) in the unconstrained conformation of ERα (chain C, PDB code
3OS8) resembling the experimentally observed structure. This binding
mode is three score units worse than the one shown above. The binding
mode of WAY6 (white sticks) is shown as reference.
Additional file 5: Figure S3. Signaling pathways differentially
expressed between breast cancer cell lines “sensitive” and “insensitive” to
YC-1 exposure (defined by the IC50 10 μM threshold). (A) High expression
of the cell cycle pathway shows significant association (false discovery
rate <5%) with YC-1 sensitivity. Pathway annotations correspond to those
in the Kyoto Encyclopedia of Genes and Genomes (KEGG). (B) High
expression of the ribosome pathway shows significant association with
lower YC-1 sensitivity.
Additional file 6: Table S3. Pathways potentially associated (false
discovery rate <5%) with the breast cancer response to YC-1.
Additional file 7: Figure S4. Analysis of ERα localization and levels
following exposure to YC-1. (A) ERα is mislocalized upon exposure to
YC-1 in both MCF7 and MCF7-LTED cells. (B) Total ERα levels are reduced
upon exposure to YC-1 in both MCF7 and MCF7-LTED cells, although
relatively more in MCF7-LTED cells. (C) Subcellular fractionation does not
Aguilar et al. Breast Cancer Research 2014, 16:R53
reveal differences for ERα. Ponceau protein staining and detection of the
62 kDa nucleoporin (NUP62) were used as loading controls.
Additional file 8: Figure S5. Expression analysis with exposure to YC-1.
(A) High expression of the Ribosome pathway (false discover rate <5%) is
shown in the parental MCF7. (B) Top panels, the Ribosome pathway is
significantly altered (that is, underexpressed) in MCF7 cells, but not in
MCF7-LTED cells, exposed to YC-1. Bottom panels, both MCF7 and
MCF7-LTED cells show underexpression of the cell cycle pathway with
exposure to YC-1. (C) Western blot analysis results of phospho-serine
235/236 S6 ribosomal protein, E2F1 and control TUBA in MCF7 and
MCF7-LTED cells in basal or YC-1-exposed conditions.
Additional file 9: Table S4. Pathways differentially expressed (false
discovery rate <5%) in MCF7 and/or MCF7-LTED cells, in basal and/or
YC-1 conditions.
Additional file 10: Table S5. Differential expression analysis of
predicted E2F1 target sets (false discovery rate <1%) in MCF7 and
MCF7-LTED cells exposed to YC-1.
Additional file 11: Figure S6. Results from RAC1 activity assays with
depletion and/or reconstitution of MYC-Vav3. Left panel, graph
depicting RAC1 activity from triplicate assays in the conditions depicted
across the x-axis. The asterisks correspond to significant differences
(P < 0.05). Right panels, Western blot analysis results of total VAV3,
MYC (for MYC-Vav3) and control TUBA in MCF7 and MCF7-LTED cells
transduced with shRNA control (pLKO.1) or shRNA-VAV3 plus MYC-Vav3
Additional file 12: Table S6. Results of the GWAS and the replication
study for SNPs in VAV3.
ChIP: Chromatin immunoprecipitation; EGFR: Epidermal growth factor
receptor; ERα: Estrogen receptor α; GSEA: Gene set expression analysis;
GWAS: Genome-wide association study; IC50: Half-maximal inhibitory
concentration; LTED: Long-term estrogen-deprived; MTT: Methylthiazol
tetrazolium; PDB: Protein Data Bank; sGC: Soluble guanylyl cyclase;
shRNA: Short hairpin RNA; SNP: Single-nucleotide polymorphism.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
HA, AU and MAP conceived the project and coordinated the experiments
and data analyses. HA, PH and RLB performed the compound screen.
JSM, NB and MAP carried out the microarray data analyses. XB performed
the protein structure analyses. AI, EN and WZ performed the ChIP data
analysis. LC, HA, MAP and LDC performed the targeted ChIP assays.
HA, NG, GM and LGB performed the cellular and molecular studies. HA
and LC performed the ESR1 shRNA-based assays. KK, TM, YN and HZ
performed the genetic association study. NG, FC, MTS, ARV, MG, AIE,
ABRP and XRB performed the tumor and immunohistochemical studies.
JBo, EK, GPT, TF, DCS and OS performed the analyses of the Swedish
breast cancer study. HA, JSM, MV, ME and MAP contributed the cell lines
and performed the erlotinib analysis. RGM, MPHMJ, JBr, AF, JBa, RC, KLB,
KEC, JAK and AV contributed the reagents and to the experimental
design. MAP drafted the manuscript. All authors read and approved
the final manuscript.
We wish to thank all study participants and their clinicians for their valuable
contributions. This work was supported by grants from the Eugenio
Rodríguez Pascual Foundation (2012, to MAP), the Government of Catalonia
(2009-SGR283, to AV and MAP), the National Institute of Diabetes and
Digestive and Kidney Diseases, National Institutes of Health (R01 DK015556,
to JAK), the Red Cooperative Research Thematic Network on Cancer (RTICC)
(12/0036/0002 to XRB and 12/0036/0008 to XRB and MAP) and the Spanish
Ministry of Health, Fund for Health Research–Institute of Health Carlos III (11/
00951 to AU and 12/01528 to MAP).
Page 14 of 16
Author details
Breast Cancer and Systems Biology Unit, Translational Research Laboratory,
Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical
Research (IDIBELL), Avda. Gran via 199, L’Hospitalet del Llobregat, Barcelona
08908, Catalonia, Spain. 2Division of Molecular Carcinogenesis, Center for
Biomedical Genetics and Cancer Genomics Centre, The Netherlands Cancer
Institute, Plesmanlaan 121, Amsterdam 1066 CX, The Netherlands. 3Center for
Genomic Medicine, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City,
Kanagawa 230-0045, Japan. 4Department of Physical Chemistry, Institute of
Biomedicine (IBUB), Avda. Diagonal 643, University of Barcelona, Barcelona
08028, Catalonia, Spain. 5Catalan Institution for Research and Advanced
Studies (ICREA), C/ Lluís Companys 23, Barcelona 08010, Catalonia, Spain.
ICO, Girona Biomedical Research Institute (IDIBGI), Hospital Josep Trueta,
Avda. França s/n, Girona 17007, Catalonia, Spain. 7Department of Genetic
Engineering and Biotechnology, University of Dhaka, Dhaka 1000,
Bangladesh. 8Centre for Genomic Regulation (CRG), C/ Dr. Aiguader 88,
Barcelona 08003, Catalonia, Spain. 9Universitat Pompeu Fabra (UPF), C/ Dr.
Aiguader 88, Barcelona 08003, Catalonia, Spain. 10Department of Molecular
Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam
1066 CX, The Netherlands. 11Department of Clinical and Experimental
Medicine, Division of Oncology, Linköping University, County Council of
Östergötland, Sandbäcksgatan 7, Linköping SE-58185, Sweden. 12Department
of Oncology, Karolinska University Hospital, Stockholm South General
Hospital, Sjukhusbacken 10, Stockholm SE-11883, Sweden. 13Department of
Pathology, Molecular Pathology Research Unit, Massachusetts General
Hospital, 13th St. Charlestown, Boston, MA 02129, USA. 14Department of Cell
Biology and Physiology, University of North Carolina at Chapel Hill, 111
Mason Farm Rd., Chapel Hill, NC 27599-7545, USA. 15Department of Medical
Oncology, Erasmus University Medical Center, Cancer Institute, PO Box 2040,
Rotterdam 3000 CA, The Netherlands. 16Translational Research Laboratory,
ICO, IDIBELL, Avda. Gran via 199, L’Hospitalet del Llobregat, Barcelona 08908,
Catalonia, Spain. 17Department of Pathology, University Hospital of Bellvitge,
IDIBELL, Avda. Feixa Llarga s/n, L’Hospitalet del Llobregat, Barcelona 08908,
Catalonia, Spain. 18Department of Medical Oncology, Breast Cancer Unit, ICO,
IDIBELL, Avda. Gran via 199, L’Hospitalet del Llobregat, Barcelona 08908,
Catalonia, Spain. 19Lombardi Comprehensive Cancer Center, Georgetown
University Medical Center, 3970 Reservoir Rd., Washington, DC 20057, USA.
Department of Molecular and Cellular Pharmacology, University of Miami,
Miller School of Medicine, 1600 NW 10th Ave., Miami, FL 33136, USA.
Department of Chemistry, University of Illinois, 505 South Mathews Ave.,
Urbana, IL 61801, USA. 22Cancer Epigenetics and Biology Program (PEBC),
IDIBELL, Avda. Gran via 199, L’Hospitalet del Llobregat, Barcelona 08908,
Catalonia, Spain. 23Department of Physiological Sciences II, School of
Medicine, University of Barcelona, Avda. Feixa Llarga s/n, L’Hospitalet del
Llobregat, Barcelona 08908, Catalonia, Spain. 24Cancer Research Center (CSIC),
University of Salamanca, Campus Miguel de Unamuno, Salamanca 37007,
Spain. 25Laboratory of Molecular Medicine, Human Genome Center, Institute
of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku,
Tokyo 108-8639, Japan. 26Present address: Onkologikoa Foundation,
Biodonostia, San Sebastián, Doctor Begiristain 121, Guipúzcoa 20014, Spain.
Received: 25 August 2013 Accepted: 16 May 2014
Published: 28 May 2014
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Cite this article as: Aguilar et al.: VAV3 mediates resistance to breast
cancer endocrine therapy. Breast Cancer Research 2014 16:R53.
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