FOXC1 Is a Potential Prognostic Biomarker with Functional
Significance in Basal-like Breast Cancer
Partha S. Ray1, Jinhua Wang2, Ying Qu2,5, Myung-Shin Sim3, Jaime Shamonki4, Sanjay P. Bagaria1,
Xing Ye3, Bingya Liu5, David Elashoff6, Dave S. Hoon2, Michael A. Walter7, John W. Martens8,
Andrea L. Richardson9, Armando E. Giuliano1, and Xiaojiang Cui2
Gene expression signatures for a basal-like breast cancer (BLBC) subtype have been associated with poor
clinical outcomes, but a molecular basis for this disease remains unclear. Here, we report overexpression of the
transcription factor FOXC1 as a consistent feature of BLBC compared with other molecular subtypes of breast
cancer. Elevated FOXC1 expression predicted poor overall survival in BLBC (P = 0.0001), independently of
other clinicopathologic prognostic factors including lymph node status, along with a higher incidence of brain
metastasis (P = 0.02) and a shorter brain metastasis–free survival in lymph node–negative patients (P < 0.0001).
Ectopic overexpression of FOXC1 in breast cancer cells increased cell proliferation, migration, and invasion,
whereas shRNA-mediated FOXC1 knockdown yielded opposite effects. Our findings identify FOXC1 as a thera-
nostic biomarker that is specific for BLBC, offering not only a potential prognostic candidate but also a
potential molecular therapeutic target in this breast cancer subtype. Cancer Res; 70(10); 3870–6. ©2010 AACR.
Molecular classification of breast cancer has identified
specific subgroups with clinical and biological implications
(1). Basal-like breast cancers (BLBC), which express genes
characteristic of basal/myoepithelial cells in the normal mam-
mary gland, compose up to 15% of all breast cancers (2).
BLBCs underexpress estrogen receptor (ER), progesterone
receptor (PR), and human epidermal growth factor receptor 2
(HER2) and encompass 60% to 90% of triple-negative (ER−/
PR−/HER2−) breast cancers. Whereas ER and HER2 guide
treatment of luminal and HER2 breast cancers, respectively,
chemotherapy is still the only modality of systemic therapy
forBLBC. Preferentially affecting youngerwomen, particularly
tologic grade, aggressive clinical behavior, and a high rate of
metastasis to the brain and lung (3). Unlike other breast cancer
subtypes, there seems to be no correlation between tumor size
and lymph node metastasis in BLBCs (4).
BLBCs are associated with expression of basal cytokeratins
(CK5/6, CK14, and CK17), epidermal growth factor receptor
(EGFR), c-kit, and p53 and absence of ER, PR, and HER2, and
thus have been defined differently in different studies using
a set of diagnostic markers. Whereas Nielsen et al. defined
BLBC on the basis of negative ER and HER2 expression
and positive basal cytokeratin, EGFR, and/or c-kit expression
(5), other groups used the combination of negative ER and
HER2 expression and positive CK5, P-cadherin, and p63 ex-
pression (6) or positive vimentin, EGFR, and CK5/6 expression
(7). These different technical approaches in combination with
widely varying patient cohorts may explain the inconsistent
experimental results for these markers.
To identify specific biomarkers for BLBC, we set out to sys-
tematically analyze the 306-member intrinsic gene set (IGS;
ref. 8), as well as other reported individual markers for
BLBC using multiple microarray data sets. Degree of corre-
lation of each individual gene with the basal-like subtype
based on mRNA expression was used to identify genes highly
specific to BLBC. The FOXC1 transcription factor emerged
as a top-ranking gene. We then assessed the diagnostic and
prognostic significance of FOXC1 and further characteri-
zed the role of FOXC1 in regulating cellular functions in
Materials and Methods
Analysis of microarray data sets. Probe-level raw expres-
sion data from publicly available human breast cancer gene
expression microarray data sets (9–18) and the ExpO Project
Authors' Affiliations: Departments of1Surgical Oncology,2Molecular
Oncology, and3Biostatistics, John Wayne Cancer Institute;4Department
of Pathology, St. John's Health Center, Santa Monica, California;
5Department of Surgery, Ruijin Hospital, Jiaotong University School of
Medicine, Shanghai, China;6Division of General Internal Medicine, School
of Medicine, University of California at Los Angeles, Los Angeles, California;
7Department of Medical Genetics, University of Alberta, Edmonton, Alberta,
Canada;8Department of Medical Oncology, Erasmus Medical Center,
Rotterdam, the Netherlands; and9Department of Pathology, Brigham and
Women's Hospital, Harvard Medical School, Boston, Massachusetts
Note: Supplementary data for this article are available at Cancer Research
P.S. Ray, J. Wang, and Y. Qu contributed equally to this work.
Corresponding Author: Xiaojiang Cui, Department of Molecular Oncology,
John Wayne Cancer Institute, Santa Monica, CA 90404. Phone: 310-998-
3916; Fax: 310-582-7390; E-mail: email@example.com.
©2010 American Association for Cancer Research.
Cancer Res; 70(10) May 15, 2010
database of the International Genomics Consortium (IGC) at
https://expo.intgen.org (Supplementary Table S1) were
downloaded and analyzed using Genespring GX 10.0 software
(Agilent Technologies). A total of 2,073 breast cancer patient
samples were analyzed. For microarray raw data obtained
from Affymetrix gene chips (8 of 11 data sets), the Robust
Multi-chip Averaging algorithm was used. Background cor-
rection, normalization, and summarization were performed,
followed by baseline transformation to median of all samples
from a specific data set on a per gene/per probe set basis. For
cDNA microarrays (3 of 11), the publicly available log 2 nor-
malized signal intensity values were directly imported into
the Genespring software platform. Molecular subtypes (lumi-
nal A/B, HER2, and basal-like) were identified by subjecting
all data sets to a hierarchical clustering algorithm by using
a Pearson uncentered similarity metric and the average link-
age rule based on the IGS (8). Average relative mRNA levels
(mean log 2 signal intensity) for the IGS genes and reported
markers for BLBC (see Supplementary Methods) were com-
pared between basal-like and pooled non–basal-like groups
using the Mann-Whitney test followed by logistic regression
analysis (SAS, version 9.1.3) to identify the genes most charac-
teristic of the basal-like group (Supplementary Tables S2–S5).
Statistical significance was defined as P < 0.05. To determine
the correlation between FOXC1 and hormone receptor sta-
tus, we used a data set that included immunohistochemical
status of ER, PR, and HER2 (11). A gene signature associated
with FOXC1 expression was developed using stringent, super-
vised inclusion criteria in five individually analyzed microarray
data sets (9, 10, 12, 13). The gene signature was additionally
validated in six other microarray data sets (refs. 11, 14–18;
see Supplementary Methods).
Survival analysis. Prognostic significance of FOXC1 in
predicting overall survival in breast cancer patients was ex-
amined in the van de Vijver et al., Herschkowitz et al., Sorlie
et al., and Pawitan et al. microarray data sets (14–16, 18). As-
sociation with metastasis to the brain or bone was examined
in lymph node–negative breast cancer patients in the Wang
et al. data set (17). The Wilcoxon rank sum test was used to
assess statistical significance for this comparison. Brain-
specific and bone-specific metastasis-free survival was also
examined in the same data set. Univariate and multivariate
analyses were done using log-rank test and Cox regression
model, respectively. Variables included in the multivariate
analysis were selected based on statistical significance in ini-
tial univariate analysis and included age, tumor size, and
lymph node status. Survival plots were created using Kaplan-
Immunohistochemistry. A polyclonal FOXC1 antibody
(Lifespan Biosciences) was used to determine FOXC1 protein
expression in human breast cancer tissue microarrays
(BRC961 and BR962, US Biomax) and in 42 archived triple-
negative human breast cancer specimens from the John
Wayne Cancer Institute tissue bank with Institutional Review
FOXC1-knockdown cells. FOXC1 shRNAs and a control
shRNA that does not match any known cDNA were from
Sigma. Cells were stably transfected with the FOXC1 or the
control shRNA construct and selected with 5 μg/mL puro-
mycin. Pooled knockdown cells were used for experiments.
FOXC1-overexpressing cells. A full-length human FOXC1
cDNA was stably transduced into breast cancer cells. Stable
cell lines were selected with 800 μg/mL G418. Pooled popu-
lations were used for experiments.
Cell culture. Cancer cell lines were from American Type
Culture Collection. Normal human mammary epithelial cells
(HMEC) were from Clonetics. Cell proliferation was assessed
by the MTT assay. Three-dimensional cell culture was done
using BD Matrigel matrix in 96-well plates.
Cell migration and invasion assay. Cells were plated on
the top of Boyden chamber inserts. Serum (10%) was used as
the chemoattractant. Cells on the lower surface of the inserts
were stained and counted. For invasion assays, inserts were
coated with Matrigel matrix.
Results and Discussion
Gene expression analysis of publicly available human breast
cancer microarray data sets revealed that the Forkhead-box
transcription factor FOXC1, essential for mesoderm tissue de-
velopment, had significantly higher expression in the basal-like
subgroup than in other subtypes (Fig. 1A and B; Supplemen-
tary Figs. S1 and S2A–C). High FOXC1 expression correlated
positively and significantly with the basal-like subgroup (Sup-
plementary Tables S2–S5). Elevated FOXC1 mRNA expres-
sion was also associated with triple-negative breast cancer,
consistent with the notion that 60% to 90% of triple-negative
breast cancers are basal-like (Fig. 1C; Supplementary
Fig. S2D). A 30-gene FOXC1 signature was derived from cor-
relation with FOXC1 expression in six data sets (Supplemen-
tary Table S6) and validated in five separate data sets. These
genes displayed an overall expression profile that coincided
with the basal-like subgroup clustered by IGS (Fig. 1D; Sup-
plementary Fig. S3). Conversely, hierarchical clustering using
the FOXC1 gene signature identified the same basal-like sub-
group determined by IGS (Supplementary Fig. S4). Whereas
pathway analysis of this gene signature did not yield a domi-
nant pathway (data not shown), some members such as
FABP7, GABRP, EN1, KCNK5, ZIC1, ACTR3B, and FOXC1
are notably involved in brain development and brain tumori-
genesis, which might explain why BLBC preferentially metas-
tasizes to the brain.
We then evaluated FOXC1 protein expression using immu-
nohistochemistry on breast cancer tissue microarrays (TMA).
Strong nuclear FOXC1 staining was found in triple-negative
TMA samples expressing basal cytokeratins (CK5/6+and/or
CK14+; Fig. 2A) but not in non–triple-negative tumors (data
not shown). Cytoplasmic staining of FOXC1 was rare, and it
was normally concomitant with nuclear staining of FOXC1.
This pattern of subcellular localization was confirmed in
an independent cohort of 42 archived triple-negative breast
cancer specimens. Positive expression of FOXC1 was associa-
ted significantly with expression of basal cytokeratins (Fig. 2B)
and displayed a sensitivity of 0.81 and a specificity of 0.80 in
detecting the basal-like phenotype identified by positive stain-
ing of CK5/6 and/or CK14. Absence of CK staining in some
FOXC1 in Basal-like Breast Cancer
Cancer Res; 70(10) May 15, 2010www.aacrjournals.org
Figure 1. Differential expression of FOXC1 in human breast cancer subtypes. A, values of normalized signal intensity (baseline-to-zero-transformed) for
basal-like subtype–associated genes from the Richardson et al. data set (9). Colors represent different subgroups: green, normal; orange, luminal A/B; blue,
HER2; red, basal-like. B, boxplot of FOXC1 values (normalized signal intensity) in normal breast tissue and luminal, HER2, and basal-like tumors of the
same data set. Statistical significance was determined using ANOVA. C, boxplot of FOXC1 values from the Hess et al. data set with known ER, PR, and
HER2 status (11). See Supplementary Fig. S2A legends for description of boxplots. Statistical significance was determined using ANOVA. D, gene
expression heat maps of the Ivshina et al. data set (12) hierarchically clustered by IGS display the expression profile of the FOXC1 signature.
Ray et al.
Cancer Res; 70(10) May 15, 2010
FOXC1+/ER−/PR−/HER2−samples in this cohort may reflect
inconsistent expression of these cytokeratins in BLBCs de-
fined by expression arrays (5). The finding that nuclear
FOXC1 was consistently detected by immunohistochemistry
despite its short protein half-life (<30 minutes; ref. 19) may
suggest a robust constitutive expression of FOXC1 in BLBC.
Analysis of a microarray data set for a human breast cancer
cell line panel revealed higher FOXC1 expression in BLBC cell
lines (Supplementary Fig. S5), which was confirmed by im-
munoblotting (Fig. 2C).
The prognostic significance of FOXC1 in breast cancer was
next examined in the 295-sample van de Vijver et al. data set
(14). In univariate analysis, overall survival was significantly
worse in tumors identified using the 30-gene FOXC1 signa-
ture (P = 0.0004) or using elevated FOXC1 mRNA levels alone
(P = 0.0001; Fig. 3A). Overall survival decreased by 35% for
each unit increase of relative FOXC1 mRNA levels. In mul-
tivariate analysis, FOXC1 was an independent prognostic
indicator of overall survival after adjusting for clinicopa-
thologic variables such as age, tumor size, and lymph node
status (hazard ratio, 1.25; 95% confidence interval, 1.02–1.52;
P = 0.02). Akaike information criteria (AIC; ref. 20) were used
in comparing the fit of the two separate prognostic models
after adjusting for clinicopathologic variables. The model
based on FOXC1 mRNA expression (AIC, 820.0) was similar
to the model based on the IGS-derived basal-like cluster
(AIC, 815) in terms of the model fit predicting survival. The
association of FOXC1 with overall survival was also shown
in the 232-sample Herschkowitz et al. (15), 122-sample Sorlie
et al. (16), and 159-sample Pawitan et al. (18) data sets (Sup-
plementary Fig. S6). Furthermore, the FOXC1 gene signature
and mRNA levels, like the basal-like phenotype, allowed prog-
nostic stratification of lymph node–negative breast can-
cers (P = 0.0003) in the van de Vijver et al. data set (ref. 14;
Fig. 3B). In addition, elevated FOXC1 expression, which
was positively associated with brain metastasis (P = 0.02)
and inversely associated with bone metastasis (P = 0.0002)
in the 286-sample Wang et al. data set (17), significantly
correlated with shorter brain metastasis–free survival
(P < 0.0001; Fig. 3C and D).
Next, we examined the function of FOXC1 in breast can-
cer cells. Overexpression of FOXC1 in MDA-MB-231 BLBC
cells (harboring moderate levels of endogenous FOXC1)
increased cell proliferation, migration, and invasion
(Fig. 4A). Similar results were observed in MCF-7 luminal
breast cancercells (harboring undetectable levels of endoge-
nous FOXC1; Supplementary Fig. S7A). FOXC1 overexpres-
sion also enhanced anchorage-independent growth of
MCF-7 cells in soft agar. Immunoblotting indicated that
cyclin D1, fibroblast markers (vimentin, fibronectin, and
Figure 2. FOXC1 protein expression in BLBC. A, representative immunohistochemical images of a basal-like sample from breast cancer tissue
microarrays stained for ER, HER2, CK5/6, CK14, and FOXC1. FOXC1 protein was not detected in non–triple-negative specimens. B, Venn diagram
showing the association between FOXC1 and cytokeratin (CK5/6 and/or CK14) immunohistochemistry status in triple-negative tumors. C, immunoblotting
of FOXC1 in normal HMECs and luminal (MCF-7, T47D, and ZR75), HER2-overexpressing (SKBR3 and HCC202), or BLBC cell lines.
FOXC1 in Basal-like Breast Cancer
Cancer Res; 70(10) May 15, 2010 www.aacrjournals.org
α-smooth muscle actin), integrins β4and β1, and matrix me-
talloproteinases MMP2 and MMP9 were upregulated by
FOXC1 overexpression (Supplementary Fig. S7B–D). FOXC1
has been shown to induce epithelial-mesenchymal transi-
tion (EMT) in MCF-12A mammary epithelial cells (21).
Similarly, FOXC1 overexpression in MCF-10A mammary
epithelial cells induced a mesenchymal phenotype accom-
panied by increased expression of the basal marker P-
cadherin and decreased expression of the epithelial marker
E-cadherin (Supplementary Fig. S7E). Regulation of these
genes by FOXC1 was also confirmed by quantitative reverse
transcription-PCR (data not shown). These data suggest that
FOXC1 can elicit an aggressive phenotype associated with
To assess the effects of FOXC1 depletion, we stably
transduced FOXC1 shRNA into 4T1 mouse breast cancer
cells, which are a model for stage IV human breast cancer
(22) and possess high levels of endogenous FOXC1 (Supple-
mentary Fig. S8A). These shRNAs reduced FOXC1 levels
by >90% (Supplementary Fig. S8B) and decreased cell
Figure 3. Prognostic significance
of FOXC1 in human breast cancer.
A, Kaplan-Meier curves of overall
survival using data from the van de
Vijver et al. data set (14). Overall
survival was stratified by molecular
subtypes (left), the FOXC1 gene
signature (middle), and FOXC1
mRNA levels (right).
B, Kaplan-Meier curves of overall
survival in lymph node–negative
patients from the same data set.
C, Kaplan-Meier curves of
brain (left) and bone (right)
metastasis–free survival using data
from the Wang et al. data set (17)
stratified by molecular subtypes.
D, Kaplan-Meier curves of brain
and bone metastasis–free survival
stratified by FOXC1 mRNA levels
from the same data set.
Ray et al.
Cancer Res; 70(10) May 15, 2010
proliferation, migration, and invasion (Fig. 4B). Similar re-
sults were obtained with BT549 human breast cancer cells
when FOXC1 was reduced by shRNA (Supplementary Fig.
S8C and D). FOXC1 depletion also converted 4T1 cells
from fibroblast-like to epithelial-like and suppressed cell
growth in three-dimensional culture and colony formation
in soft agar (Fig. 4C and D). These data further suggest a
role of FOXC1 in regulation of cell function.
Studies have suggested that BLBC may possess extraordi-
narily high growth rates (23) and an EMT phenotype (24)
compared with other breast cancer subgroups. FOXC1 may
play a role in coordinating these BLBC properties. The
mechanism for exclusive induction of FOXC1 in BLBC is
not clear. A recent high-resolution array comparative ge-
nomic hybridization analysis revealed that the FOXC1 gene
is not amplified in the basal-like tumors (25). Interestingly,
FOXC1 is one of the genes highly expressed and hypomethy-
lated in CD44+CD24−breast cancer cells (21); however,
CD44+CD24−cells are also present in nonbasal subtypes.
Whether DNA methylation plays a dominant role in BLBC-
associated FOXC1 expression remains to be determined. The
exclusive expression of FOXC1 in BLBC may be due to multi-
ple regulatory mechanisms. In summary, our study suggests
that FOXC1 may be a potentially significant diagnostic and
prognostic biomarker for BLBC and may serve as a therapeutic
target for BLBC.
Figure 4. Effects of FOXC1 overexpression and knockdown in breast cancer cells. A, cell proliferation (left), migration (middle), and invasion (right)
of FOXC1- or vector-overexpressing MDA-MB-231 cells. Columns, mean (n = 3); bars, SD. *, P < 0.05, versus the control. B, cell proliferation, migration,
and invasion of control or FOXC1 shRNA–expressing 4T1 cells. *, P < 0.05, versus the control. C, morphologies of control and FOXC1 shRNA 4T1
cells in monolayer culture. D, representative images of control and FOXC1 shRNA 4T1 cells grown in three-dimensional (3-D) Matrigel (left) and soft
agar (right). Bar, 135 μm.
FOXC1 in Basal-like Breast Cancer
Cancer Res; 70(10) May 15, 2010 www.aacrjournals.org
Disclosure of Potential Conflicts of Interest Download full-text
No potential conflicts of interest were disclosed.
We thank Fred Miller for 4T1 breast cancer cells.
Susan G. Komen Foundation, Avon Foundation, and George Adler Research
Fund (X. Cui).
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 11/10/2009; revised 02/25/2010; accepted 03/02/2010; published
Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human
breast tumours. Nature 2000;406:747–52.
Kreike B, van Kouwenhove M, Horlings H, et al. Gene expression
profiling and histopathological characterization of triple-negative/
basal-like breast carcinomas. Breast Cancer Res 2007;9:R65.
Carey LA, Perou CM, Livasy CA, et al. Race, breast cancer subtypes,
and survival in the Carolina Breast Cancer Study. JAMA 2006;295:
Dent R, Trudeau M, Pritchard KI, et al. Triple-negative breast cancer:
clinical features and patterns of recurrence. Clin Cancer Res 2007;
Nielsen TO, Hsu FD, Jensen K, et al. Immunohistochemical and
clinical characterization of the basal-like subtype of invasive breast
carcinoma. Clin Cancer Res 2004;10:5367–74.
Elsheikh SE, Green AR, Rakha EA, et al. Caveolin 1 and caveolin 2 are
associated with breast cancer basal-like and triple-negative immuno-
phenotype. Br J Cancer 2008;99:327–34.
Livasy CA, Karaca G, Nanda R, et al. Phenotypic evaluation of the
basal-like subtype of invasive breast carcinoma. Mod Pathol 2006;
Hu Z, Fan C, Oh DS, et al. The molecular portraits of breast tumors
are conserved across microarray platforms. BMC Genomics 2006;
Richardson AL, Wang ZC, De Nicolo A, et al. X chromosomal ab-
normalities in basal-like human breast cancer. Cancer Cell 2006;9:
10. Farmer P, Bonnefoi H, Becette V, et al. Identification of molecular
apocrine breast tumours by microarray analysis. Oncogene 2005;
11. Hess KR, Anderson K, Symmans WF, et al. Pharmacogenomic pre-
dictor of sensitivity to preoperative chemotherapy with paclitaxel and
fluorouracil, doxorubicin, and cyclophosphamide in breast cancer.
J Clin Oncol 2006;24:4236–44.
12. Ivshina AV, George J, Senko O, et al. Genetic reclassification of
histologic grade delineates new clinical subtypes of breast cancer.
Cancer Res 2006;66:10292–301.
13. Miller LD, Smeds J, George J, et al. An expression signature for p53
status in human breast cancer predicts mutation status, transcrip-
tional effects, and patient survival. Proc Natl Acad Sci U S A 2005;
14. van de Vijver MJ, He YD, van't Veer LJ, et al. A gene-expression sig-
nature as a predictor of survival in breast cancer. N Engl J Med 2002;
15. Herschkowitz JI, Simin K, Weigman VJ, et al. Identification of con-
served gene expression features between murine mammary carcino-
ma models and human breast tumors. Genome Biol 2007;8:R76.
16. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast
tumor subtypes in independent gene expression data sets. Proc Natl
Acad Sci U S A 2003;100:8418–23.
17. Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict
distant metastasis of lymph-node-negative primary breast cancer.
18. Pawitan Y, Bjohle J, Amler L, et al. Gene expression profiling spares
early breast cancer patients from adjuvant therapy: derived and val-
idated in two population-based cohorts. Breast Cancer Res 2005;7:
19. Berry FB, Mirzayans F, Walter MA. Regulation of FOXC1 stability
and transcriptional activity by an epidermal growth factor-activated
mitogen-activated protein kinase signaling cascade. J Biol Chem
20. Akaike H. A new look at the statistical model identification. IEEE
Trans Automatic Control 1974;19:716–23.
21. Bloushtain-Qimron N, Yao J, Snyder EL, et al. Cell type-specific DNA
methylation patterns in the human breast. Proc Natl Acad Sci U S A
22. Aslakson CJ, Miller FR. Selective events in the metastatic process
defined by analysis of the sequential dissemination of subpopula-
tions of a mouse mammary tumor. Cancer Res 1992;52:1399–405.
23. Seewaldt VL, Scott V. Images in clinical medicine. Rapid progression
of basal-type breast cancer. N Engl J Med 2007;356:e12.
24. Sarrio D, Rodriguez-Pinilla SM, Hardisson D, Cano A, Moreno-Bueno
G, Palacios J. Epithelial-mesenchymal transition in breast cancer
relates to the basal-like phenotype. Cancer Res 2008;68:989–97.
25. Andre F, Job B, Dessen P, et al. Molecular characterization of breast
cancer with high-resolution oligonucleotide comparative genomic
hybridization array. Clin Cancer Res 2009;15:441–51.
Ray et al.
Cancer Res; 70(10) May 15, 2010