U.S. patent application number 12/852453 was filed with the patent office on 2011-06-23 for methods for diagnosis, prognosis and treatment of primary and metastatic basal-like breast cancer and other cancer types.
Invention is credited to Sanjay Bagaria, Xiaojiang Cui, Partha S. Ray, Jinhua Wang.
Application Number | 20110150979 12/852453 |
Document ID | / |
Family ID | 43544702 |
Filed Date | 2011-06-23 |
United States Patent
Application |
20110150979 |
Kind Code |
A1 |
Ray; Partha S. ; et
al. |
June 23, 2011 |
METHODS FOR DIAGNOSIS, PROGNOSIS AND TREATMENT OF PRIMARY AND
METASTATIC BASAL-LIKE BREAST CANCER AND OTHER CANCER TYPES
Abstract
In one embodiment, a method of theranostic classification of a
breast cancer tumor is provided, comprising obtaining a breast
cancer tumor sample from a subject, detecting an expression level
of FOXC1, comparing the expression level of FOXC1 to a
predetermined cutoff level, and classifying the breast cancer tumor
sample as belonging to a theranostic basal-like breast cancer tumor
subtype or a theranostic hybrid basal-like breast cancer tumor
subtype when the expression level of FOXC1 is higher than the
predetermined cutoff level. In other embodiments, methods for
predicting a prognosis of a basal-like breast cancer and methods of
treating a basal-like breast cancer are provided,
Inventors: |
Ray; Partha S.; (Los
Angeles, IL) ; Bagaria; Sanjay; (Jacksonville,
FL) ; Cui; Xiaojiang; (Pearland, TX) ; Wang;
Jinhua; (Los Angeles, CA) |
Family ID: |
43544702 |
Appl. No.: |
12/852453 |
Filed: |
August 7, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US10/44817 |
Aug 6, 2010 |
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12852453 |
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61231984 |
Aug 6, 2009 |
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Current U.S.
Class: |
424/450 ;
424/133.1; 424/174.1; 435/6.1; 435/6.12; 514/44A; 514/648 |
Current CPC
Class: |
C12Q 2600/118 20130101;
C12Q 2600/158 20130101; C12Q 1/6886 20130101; C12Q 2600/106
20130101; C12Q 2600/112 20130101; A61P 35/00 20180101; C12Q 1/6881
20130101 |
Class at
Publication: |
424/450 ;
435/6.1; 435/6.12; 514/44.A; 424/174.1; 424/133.1; 514/648 |
International
Class: |
A61K 9/127 20060101
A61K009/127; C12Q 1/68 20060101 C12Q001/68; A61K 31/713 20060101
A61K031/713; A61K 31/7088 20060101 A61K031/7088; A61K 39/395
20060101 A61K039/395; A61K 31/138 20060101 A61K031/138; A61P 35/00
20060101 A61P035/00 |
Claims
1. A method of theranostic classification of a breast cancer tumor,
the method comprising: obtaining a breast cancer tumor sample from
a subject; detecting an expression level of FOXC1; comparing the
expression level of FOXC1 to a predetermined cutoff level; and
classifying the breast cancer tumor sample as belonging to a
theranostic basal-like breast cancer tumor subtype or a theranostic
hybrid basal-like breast cancer tumor subtype when the expression
level of FOXC1 is higher than the predetermined cutoff level.
2. The method of claim 1, wherein the breast cancer tumor sample is
a formalin-fixed paraffin embedded (FFPE) sample.
3. The method of claim 2, wherein the expression level of FOXC1 is
determined by quantitative reverse transcriptase polymerase chain
reaction (qRT-PCR) or a Quantigene.RTM. FFPE assay.
4. The method of claim 1, wherein the predetermined cutoff level is
determined by a 90th percentile level of FOXC1 expression levels
for a dataset of breast cancer tumors, the dataset comprising all
breast cancer subtypes.
5. The method of claim 1, further comprising: determining an
expression status for estrogen receptor (ER), progesterone receptor
(PR) and human epidermal growth factor receptor 2 (HER2); and
classifying the breast cancer tumor sample as belonging to a
theranostic hybrid basal-like/HER2+ breast cancer tumor subtype
when the expression status of ER is negative (ER-), the expression
status of PR is negative (PR-), the expression status of HER2 is
positive (HER2+) and the expression level of FOXC1 is higher than
the predetermined cutoff level;
6. The method of claim 5, wherein the predetermined cutoff level is
determined by a 50th percentile level of FOXC1 expression levels
for a dataset of breast cancer tumors, the dataset comprising
tumors having a HER2+ status.
7. The method of claim 1, further comprising: determining an
expression status of ER, PR, and HER2 of the breast cancer tumor
sample; and classifying the breast cancer tumor sample as belonging
to a theranostic hybrid basal-like/triple-negative breast cancer
tumor subtype when the expression status of ER is negative (ER-),
the expression status of PR is negative (PR-), the expression
status of HER2 is negative (HER2-) and the expression level of
FOXC1 is higher than the predetermined cutoff level.
8. The method of claim 7A5, wherein the predetermined cutoff level
is determined by a 50th percentile level of FOXC1 expression levels
for a dataset of breast cancer tumors, the dataset comprising
tumors having an ER-/PR-/HER2- status.
9. The method of claim 1, further comprising: determining an
expression status of ER, PR, and HER2 of the breast cancer tumor
sample; and classifying the breast cancer tumor sample as belonging
to a theranostic hybrid basal-like/luminal breast cancer tumor
subtype when the expression status of ER is positive (ER+), the
expression status of PR is negative or positive (PR-/PR+), the
expression status of HER2 is negative or positive (HER2-/HER2+) and
the expression level of FOXC1 is higher than the predetermined
cutoff level.
10. The method of claim 9A7, wherein the predetermined cutoff level
is determined by a 50th percentile level of FOXC1 expression levels
for a dataset of breast cancer tumors, the dataset comprising
tumors having an ER+ status.
11. A method for predicting a prognosis of a basal-like breast
cancer, the method comprising: obtaining a breast cancer tumor
sample from a subject; detecting an expression level of FOXC1;
comparing the expression level of FOXC1 to a predetermined cutoff
level; predicting a poor prognosis of the basal-like breast cancer
when the expression level of FOXC1 is higher than the predetermined
cutoff level.
12. The method of claim 11, wherein the breast cancer tumor sample
is a formalin-fixed paraffin embedded (FFPE) sample.
13. The method of claim 12, wherein the expression level of FOXC1
is determined by quantitative reverse transcriptase polymerase
chain reaction (qRT-PCR) or a Quantigene.RTM. FFPE assay.
14. The method of claim 11, wherein the predetermined cutoff level
is determined by a 90th percentile level of FOXC1 expression levels
for a dataset of breast cancer tumors, the dataset comprising all
breast cancer subtypes.
15. The method of claim 11, wherein the basal-like breast cancer is
a hybrid basal-like/HER2+ breast cancer and the predetermined
cutoff level is determined by a 50th percentile level of FOXC1
expression levels for a dataset of breast cancer tumors, the
dataset comprising tumors having a HER2+ status.
16. The method of claim 11, wherein the basal-like breast cancer is
a hybrid basal-like/luminal breast cancer and the predetermined
cutoff level is determined by a 50th percentile level of FOXC1
expression levels for a dataset of breast cancer tumors, the
dataset comprising tumors having an ER+ status.
17. The method of claim 11, wherein the basal-like breast cancer is
a hybrid basal-like/triple-negative breast cancer and the
predetermined cutoff level is determined by a 50th percentile level
of FOXC1 expression levels for a dataset of breast cancer tumors,
the dataset comprising tumors having an ER-/PR-/HER2- status.
18. The method of claim 11, wherein the prognosis is overall
survival or recurrence free survival.
19. The method of claim 11, wherein the prognosis is a propensity
of developing a distant metastasis or a time to a distant
metastasis.
20. The method of claim 19 wherein the distant metastasis is brain
metastasis.
21. The method of claim 11, wherein the prognosis is a propensity
for resistance to a targeted cancer therapy.
22. The method of claim 21, wherein the targeted therapy is a
substance that inhibits HER2 expression and/or activity.
23. The method of claim 22, wherein the substance is a trastuzumab
(Herceptin.RTM.).
24. The method of claim 21, wherein the targeted therapy is a
substance that inhibits ER expression and/or activity.
25. The method of claim 24, wherein the substance is tamoxifen or
an aromatase inhibitor.
26. A method of treating a basal-like breast cancer comprising:
administering to a subject having a basal-like breast cancer a
pharmaceutical composition, the composition comprising a
pharmaceutically acceptable carrier and a therapeutically effective
amount of a substance that inhibits FOXC1 expression and/or
activity.
27. The method of claim 26, wherein the basal-like breast cancer is
a hybrid basal-like/triple-negative breast cancer tumor
subtype.
28. The method of claim 26, wherein the pharmaceutically acceptable
carrier is a PEGylated immunoliposome that encapsulates the
substance.
29. The method of claim 26, wherein the substance is selected from
the group consisting of an anti-FOXC1 antibody or functional
fragment thereof, a small molecule or an anti-FOXC1 shRNA, siRNA or
RNAi.
30. The method of claim 26, wherein the basal-like breast cancer is
a hybrid basal-like/HER2+ breast cancer tumor subtype.
31. The method of claim 30, wherein the pharmaceutical composition
further comprises a therapeutically effective amount of a substance
that inhibits HER2 expression and/or activity.
32. The method of claim 31, wherein the substance that inhibits
HER2 expression and/or activity is trastuzumab
(Herceptin.RTM.).
33. The method of claim 26, wherein the basal-like breast cancer is
a hybrid basal-like/luminal breast cancer tumor subtype.
34. The method of claim 33, wherein the pharmaceutical composition
further comprises a therapeutically effective amount of a substance
that inhibits ER expression and/or activity.
35. The method of claim 34, wherein the substance that inhibits ER
expression and/or activity is tamoxifen or an aromatase inhibitor.
Description
PRIORITY CLAIM
[0001] This application is a continuation of International
Application No. PCT/US10/44817, filed on Aug. 6, 2010, and also
claims priority to U.S. Provisional Patent Application No.
61/231,984, filed on Aug. 6, 2009, both of which are incorporated
by reference as if fully set forth herein.
BACKGROUND
[0002] Diversity of molecular alterations, cellular compositions
and clinical outcomes in cancer creates a major challenge in cancer
treatment with respect to providing accurate diagnostic,
prognostic, and predictive information. Tumors are typically
described histopathologically using the tumor-node-metastasis (TNM)
system. This system, which uses the size of the tumor, the presence
or absence of tumor in regional lymph nodes, and the presence or
absence of distant metastases, assigns a stage to the tumor as
described by the American Joint Committee on Cancer (AJCC). The
assigned stage is used as the basis for prognostication and for
selection of appropriate therapy. However, this approach has many
limitations. Tumors with similar TNM stage and histopathologic
appearance can exhibit significant variability in terms of clinical
course and response to therapy. For example, some tumors are very
aggressive while others are not. Some tumors respond readily to
hormonal therapy or chemotherapy while others are resistant.
[0003] The use of tumor biomarkers has provided an additional
approach for dividing certain tumor types into subclasses. For
example, one factor considered in prognosis and in treatment
decisions for breast cancer is the presence or absence of the
estrogen receptor (ER) in tumor samples. ER-positive breast cancers
typically respond much more readily to hormonal therapies such as
tamoxifen than ER-negative tumors. Though useful, this biomarker
provides information for only a specific subset of breast cancers,
leaving other subsets unaddressed.
[0004] Gene expression profiling has been successful in delineating
specific breast cancer intrinsic molecular subtypes (Perou et al.
2000). This represents a significant advance in the understanding
of breast cancer, the most commonly diagnosed cancer in women
worldwide (Landis et al., 1999) and a disease that has proven to be
quite heterogeneous in terms of its clinical presentation and
features. Groups of breast cancer patients with distinct
differences in their prognostic profiles have now been found to
have equally distinct biologic and/or molecular profiles to help
explain their associated clinical outcomes. This offers a
tremendous opportunity to develop personalized therapeutics
targeting the specific tumor biology associated with a specific
molecular subtype of breast cancer. One particular molecular
subtype that has garnered considerable interest is basal-like
breast cancer (BLBC).
[0005] Although first reported more than 20 years ago on the basis
of immunohistochemical (IHC) detection of basal cytokeratins (CK),
this subtype again became notable after transcriptomic analysis of
breast cancer confirmed its existence as a distinct molecular
entity within breast cancer. While it differs substantially from
the other delineated molecular subtypes in terms of its molecular
makeup, the reason it has captured the attention of cancer
biologists and clinicians alike is on account of its uniformly poor
prognosis and lack of targeted therapy options. BLBC displays
significant overlap with "triple-negative" breast cancer--a
pathologic entity defined based on the absence of well-known breast
cancer biomarkers estrogen receptor (ER), progesterone receptor
(PR) and human epidermal growth factor receptor-2 (HER2). It is
estimated that 60% to 90% of triple-negative breast cancers are
BLBC. However BLBC is not synonymous with triple-negative breast
cancer. Patients with BLBC are often younger, are more likely to be
of African-American descent (Carey et al. 2006; Ihemelandu et al.
2007; Ihemelandu et al. 2008), are more likely to be BRCA1 mutation
positive (Rakha et al. 2009), frequently develop distant metastatic
disease to the brain and/or lung within 3-5 years of initial
presentation (Wang et al. 2005) and have poor overall survival
(Carey et al. 2006). In fact, the development of distant metastatic
disease and subsequent death appears to be independent of initial
presenting nodal status, as the majority of patients are lymph node
negative at the time of initial diagnosis (Dent et al. 2007).
[0006] Currently the most effective biomarkers in routine clinical
practice are theranostic biomarkers. Theranostic biomarkers provide
information with respect to diagnosis (determination of the cancer
biologic subtype), prognosis (determination of the clinical
outcome) and therapeutic prediction (determination of therapeutic
efficacy). Theranostic biomarkers are functionally most central and
pivotal in the network of biomolecules that control the biology of
their specific biologic subtype. Hence, targeted therapy directed
towards a theranostic biomarker has a profound effect on clinical
outcomes.
[0007] In breast cancer an example of a theranostic biomarker is
ER. It accurately diagnoses "luminal" breast cancer patients
(ER-positive), accurately prognosticates their outcome, and
predicts their favorable response to tamoxifen, a drug that
specifically targets ER. Prior to the introduction of tamoxifen
therapy, ER-positive breast cancer patients had a poor prognosis.
Their prognosis dramatically improved after therapy with tamoxifen
became standard of care for such patients. Therefore, the most
important component of a theranostic biomarker is the diagnosis it
offers. Because with diagnosis comes prediction of therapeutic
efficacy, which ultimately determines patient prognosis. While
prognosis may change depending on advancements in therapy, the
diagnosis of a biologic subtype, and therefore its target(s) for
therapy will remain immutable. Moreover, the prognosis offered by a
theranostic biomarker is more accurate than that offered by a
non-theranostic biomarker. This is because theranostic biomarkers
predict clinical outcomes that are very specific to the biology of
the cancer subtype. For example, ER-positive status very
specifically reflects the current favorable prognosis associated
only with the luminal subtype because it takes into account
subtype-specific treatment with anti-ER therapy (e.g. tamoxifen).
Therefore, theranostic biomarkers offer superior prognosis.
[0008] Whole genome profiling technologies such as gene expression
profiling (transcriptomics) have greatly expanded our knowledge of
the genes and genetic pathways associated with the development and
progression of cancer. Based on this knowledge, several
commercialized multigene prognostic tests have entered the complex
and expanding landscape of the cancer in vitro diagnostics (IVD)
market. These tests contain many genes, only some of which indeed
have critical functional importance to the survival and maintenance
of the malignant phenotype. Such tests are unable to offer a
refined understanding of the underlying biology of a specific
subtype. In other words, the main drawback of such multigene
prognostic tests is that they are not theranostic. They do not
provide a diagnosis of a specific biologic subtype, and therefore
they do not offer insight with regard to subtype-specific
treatment. As a result, the prognostic value they offer is only an
approximation across multiple subtypes. This is in contrast to a
theranostic biomarker whose prognostic value is derived from a
single subtype, and is therefore more precise and accurate.
[0009] Therefore the discovery and elucidation of theranostic
biomarkers for BLBC and other cancers is important for the
improvement of the classification of tumors and the treatment of
cancer patients.
SUMMARY
[0010] In one embodiment, a method of theranostic classification of
a breast cancer tumor is provided, the method comprising obtaining
a breast cancer tumor sample from a subject, detecting an
expression level of FOXC1, comparing the expression level of FOXC1
to a predetermined cutoff level, and classifying the breast cancer
tumor sample as belonging to a theranostic basal-like breast cancer
tumor subtype or a theranostic hybrid basal-like breast cancer
tumor subtype when the expression level of FOXC1 is higher than the
predetermined cutoff level.
[0011] In one embodiment, the method of theranostic classification
of a breast cancer tumor may further comprise determining an
expression status for estrogen receptor (ER), progesterone receptor
(PR) and human epidermal growth factor receptor 2 (HER2) and
classifying the breast cancer tumor sample as belonging to a
theranostic hybrid basal-like/HER2+ breast cancer tumor subtype
when the expression status of ER is negative (ER-), the expression
status of PR is negative (PR-), the expression status of HER2 is
positive (HER2+) and the expression level of FOXC1 is higher than
the predetermined cutoff level.
[0012] In another embodiment, the method of theranostic
classification of a breast cancer tumor may further comprise
determining an expression status of ER, PR, and HER2 of the breast
cancer tumor sample and classifying the breast cancer tumor sample
as belonging to a theranostic hybrid basal-like/triple-negative
breast cancer tumor subtype when the expression status of ER is
negative (ER-), the expression status of PR is negative (PR-), the
expression status of HER2 is negative (HER2-) and the expression
level of FOXC1 is higher than the predetermined cutoff level.
[0013] In another embodiment, the method of theranostic
classification of a breast cancer tumor may further comprise
determining an expression status of ER, PR, and HER2 of the breast
cancer tumor sample and classifying the breast cancer tumor sample
as belonging to a theranostic hybrid basal-like/luminal breast
cancer tumor subtype when the expression status of ER is positive
(ER+), the expression status of PR is negative or positive
(PR-/PR+), the expression status of HER2 is negative or positive
(HER2-/HER2+) and the expression level of FOXC1 is higher than the
predetermined cutoff level.
[0014] In one embodiment, a method for predicting a prognosis of a
basal-like breast cancer is provided, the method comprising
obtaining a breast cancer tumor sample from a subject, detecting an
expression level of FOXC1, comparing the expression level of FOXC1
to a predetermined cutoff level, and predicting a poor prognosis of
the basal-like breast cancer when the expression level of FOXC1 is
higher than the predetermined cutoff level.
[0015] In some embodiments, the basal-like breast cancer is a
hybrid basal-like/HER2+ breast cancer and the predetermined cutoff
level is determined by a 50th percentile level of FOXC1 expression
levels for a dataset of breast cancer tumors, the dataset
comprising tumors having a HER2+ status.
[0016] In other embodiments, the basal-like breast cancer is a
hybrid basal-like/luminal breast cancer and the predetermined
cutoff level is determined by a 50th percentile level of FOXC1
expression levels for a dataset of breast cancer tumors, the
dataset comprising tumors having an ER+ status.
[0017] In other embodiments, the basal-like breast cancer is a
hybrid basal-like/triple-negative breast cancer and the
predetermined cutoff level is determined by a 50th percentile level
of FOXC1 expression levels for a dataset of breast cancer tumors,
the dataset comprising tumors having an ER-/PR-/HER2- status.
[0018] In some embodiments, the prognosis is overall survival,
recurrence free survival, a propensity of developing a distant
metastasis, a time to a distant metastasis such as brain
metastasis, a propensity for resistance to a targeted cancer
therapy (e.g., trastuzumab (Herceptin.RTM., tamoxifen or an
aromatase inhibitor), wherein a propensity for resistance to a
targeted cancer therapy may be a predictor of resistance or
decreased efficacy.
[0019] In one embodiment, a method of treating a basal-like breast
cancer is provided, the method comprising administering to a
subject having a basal-like breast cancer a pharmaceutical
composition, the composition comprising a pharmaceutically
acceptable carrier and a therapeutically effective amount of a
substance that inhibits FOXC1 expression and/or activity. In one
embodiment, the basal-like breast cancer being treated is a hybrid
basal-like/triple-negative breast cancer tumor subtype such as a
hybrid basal-like/HER2+ breast cancer tumor subtype or hybrid
basal-like/luminal breast cancer tumor subtype.
[0020] In one embodiment, the pharmaceutically acceptable carrier
is a PEGylated immunoliposome that encapsulates the substance. In
another embodiment, the substance is selected from the group
consisting of an anti-FOXC1 antibody or functional fragment
thereof, a small molecule or an anti-FOXC1 shRNA, siRNA or
RNAi.
[0021] In some embodiments, the pharmaceutical composition further
comprises a therapeutically effective amount of a substance that
inhibits HER2 expression and/or activity such as trastuzumab
(Herceptin.RTM.). In other embodiments, the pharmaceutical
composition further comprises a therapeutically effective amount of
a substance that inhibits ER expression and/or activity such as
tamoxifen or an aromatase inhibitor.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] FIG. 1 shows 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 (Richardson et al. 2006).
Numbers represent different subgroups: (1), normal; (2), luminal
A/B; (3), HER2; (4), 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
(Hess et al. 2006). See FIG. 3 legends for description of boxplots.
Statistical significance was determined using ANOVA. D, gene
expression heat maps of the Ivshina et al. data set (Ivshina et al.
2006) hierarchically clustered by IGS display the expression
profile of the FOXC1 signature.
[0023] FIG. 2 shows differential expression of FOXC1 in human
breast cancer subtypes. Values of normalized signal intensity for
12 reported basal-like markers from a representative dataset
(Richardson et al. 2006) are presented. Numbers represent different
subgroups: (1), normal; (2), luminal A/B; (3), HER2; (4),
Basal-like. The corresponding heat map is shown below.
[0024] FIG. 3 shows differential expression of FOXC1 according to
molecular subtypes or triple negative status. A. Boxplot of FOXC1
values (normalized signal intensity) in luminal A/B, HER2, and
basal-like tumors of the Ivshina et al. dataset (Ivshina et al.
2006). The line in the center of each box represents the median
value of the distribution, and the upper and lower ends of the box
are the upper (75th) and lower (25th) quartiles, respectively. The
whiskers extend to the most extreme data point that is less than
1.5 times the interquartile range from the box. Statistical
significance was determined using ANOVA. Table of FOXC1 high
(>90th percentile) and FOXC1 low (<90th percentile) status
versus molecular subtypes. Chi square P<0.0001. B. Boxplot of
FOXC1 values (normalized signal intensity) in luminal A/B, HER2,
and basal-like tumors of the Miller et al. dataset (Miller et al.
2005). Statistical significance was determined using ANOVA. Table
of FOXC1 high (>90th percentile) and FOXC1 low (<90th
percentile) status versus molecular subtypes. Chi square
P<0.0001. C. Boxplot of FOXC1 values (normalized signal
intensity) in luminal A/B, HER2, and basal-like tumors of the van
de Vijver et al. dataset (van de Vijver et al. 2002). Statistical
significance was determined using ANOVA. Table of FOXC1 high
(>90th percentile) and FOXC1 low (<90th percentile) status
versus molecular subtypes. Chi square P<0.0001. D. Boxplot of
FOXC1 values (normalized signal intensity) in triple-negative and
non-triple-negative tumors of the Hess et al. dataset (Hess et al.
2006). Statistical significance was determined using ANOVA. Table
of FOXC1 high (>90th percentile) and FOXC1 low (<90th
percentile) status versus triple-negative status. Chi square
P<0.0001.
[0025] FIG. 4 shows an association of the FOXC1 gene signature with
basal-like breast cancer. Gene expression heat maps of a 251-sample
human breast cancer cDNA microarray dataset (Miller et al. 2005)
hierarchically clustered by IGS demonstrate the overall expression
profile of the FOXC1-associated 30 genes.
[0026] FIG. 5 shows unsupervised clustering by the FOXC1 gene
signature identifies the basal-like subgroup. A 249-sample human
breast cancer cDNA microarray dataset (Ivshina et al. 2006) was
clustered by IGS and the FOXC1 gene signature respectively. The
basal-like subtype clusters are indicated with red bars.
[0027] FIG. 6 illustrates 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.
[0028] FIG. 7 illustrates that FOXC1 is overexpressed in basal-like
breast cancer cell lines. Gene expression heat map from cDNA
microarray analysis of 51 human breast cancer cell lines. Displayed
is the same panel of 12 genes as in FIG. 2. MSN--Moesin,
KRT5--Cytokeratin 5/CK5/6, CDH3--P-cadherin,
CRYAB--.alpha.B-crystallin, KRT14--cytokeratin 14/CK14,
KRT17--cytokeratin 17/CK17.
[0029] FIG. 8 illustrates 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 (van de Vijver et al.
2002). 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 (Wang et al. 2005)
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.
[0030] FIG. 9 illustrates prognostic power of FOXC1 expression in
human breast cancers. A, Kaplan-Meier curves of overall survival
using data from a 232-sample microarray dataset (Herschkowitz et
al. 2007) with linked clinical information. B, Kaplan-Meier curves
of overall survival using data from a 122-sample microarray dataset
(Sorlie et al. 2003) with linked clinical information. C,
Kaplan-Meier curves of overall survival using data from a
159-sample microarray dataset (Pawitan et al. 2005) with linked
clinical information. Overall survival is displayed according to
molecular subtypes (left) and FOXC1 mRNA levels (right).
[0031] FIG. 10 is a receiver operator curve (ROC)-area under curve
(AUC) for FOXC1 expression in predicting basal-like breast cancer.
(Parker et al. 2009)
[0032] FIG. 11 shows the 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 .mu.m.
[0033] FIG. 12 shows the effects of FOXC1 overexpression in human
breast cancer cells and MCF-10A cells. A, FOXC1 was stably
transfected into MCF-7 breast cancer cells. Cell proliferation
(left), anchorage-independent growth (middle-left), migration
(middle-right), and invasion (right) of FOXC1- or vector-expressing
cells were measured using MTT, soft agar, and Boyden chamber
assays. *, P<0.05 versus the vector control. B, expression of
cyclin D1 and fibroblastic markers in MDA-MB-231 cells
overexpressing FOXC1 or the control vector was examined by
immunoblotting. C, levels of (34 and (31 integrins in MDA-MB-231
cells overexpressing FOXC1 or the control vector were measured by
flow cytometry. *, P<0.05 versus the vector control. Of note,
same results were obtained with MCF-7 cells. D, expression of MMP2
and MMP9 was measured by ELISA. Each bar represents mean.+-.SD
(n=3). *, P<0.05 versus the vector control. E, morphologies of
MCF-10A human mammary epithelial cells overexpressing the vector or
FOXC1 (left) and immunoblotting of luminal (E-cadherin) and basal
(P-cadherin) markers in the same cells.
[0034] FIG. 13 shows the effects of FOXC1 knockdown in human breast
cancer cells. A, FOXC1 protein levels were compared in MCF-7,
BT549, and 4T1 breast cancer cells (refer to FIG. 2C). B,
immunoblotting of FOXC1 in 4T1 cells expressing control or FOXC1
shRNA. C, cell proliferation (left), migration (middle), and
invasion (right) of control or FOXC1 shRNA19 expressing BT549 cells
were measured using MTT and Boyden chamber assays. *, P<0.05
versus the control. D, immunoblotting of FOXC1 in BT549 cells
expressing control or FOXC1 shRNA.
[0035] FIG. 14 is a Kaplan-Meier curve of overall survival using
microarray data from 58 HER2-amplified tumors. Semiquantitative
FOXC1 mRNA expression above the 50.sup.th percentile was found to
be a significant predictor of poor survival (p=0.0313 on univariate
analysis). On multivariate analysis, when controlled for age, tumor
size and nodal status, FOXC1 mRNA expression greater than 50.sup.th
percentile cutoff value was an independent prognosticator of poor
survival (HR 2.54, 95% CI 1.21-5.33, p=0.0138). Nodal status and
age were not significant prognosticators on multivariate analysis
(Staaf et al. 2010).
[0036] FIG. 15 is a flow diagram of patient identification, sample
collection and tissue processing for immunohistochemical assessment
of ER, PR, HER2, CK5/6, CK14 and FOXC1.
[0037] FIG. 16 shows Kaplan Meier curves of 5-year overall survival
of breast cancer patients grouped according to (A) FOXC1 protein
expression status as assessed on standard immunohistochemistry,
wherein positive expression of FOXC1 was shown to be a significant
predictor of overall survival, independent of the cutoff value
employed; and (B) surrogate immunohistochemical biomarker models of
molecular subtype utilizing 3 different cutoff values to define
positive expression of FOXC1. Level of protein expression as
assessed by IHC was given a score of 0 (negligible or no
expression) to 3 (high expression). The three cutoff values were: 0
vs. 1, 2, 3; 0, 1 vs. 2, 3; or 1, 2, 3 vs. 3.
[0038] FIG. 17 shows Kaplan Meier curves of 5-year overall survival
of breast cancer patients grouped according to (A) Triple negative
phenotype (TNP) status, Basal cytokeratin (CK) expression status
and FOXC1 protein expression status as assessed on standard
immunohistochemistry; and (B) 3 surrogate immunohistochemical
biomarker panel models of breast cancer molecular subtype--1) the
classic 3-biomarker panel comprising of ER, PR and HER2, 2) a
5-biomarker panel comprising of the above receptors in combination
with traditional basal-like biomarkers, basal CK5/6 and CK14, and
3) a 4-biomarker panel comprising of ER, PR and HER2, in
combination with FOXC1.
[0039] FIG. 18 illustrates that FOXC1 expression is negatively
associated with ER.alpha. expression in human breast cancer. (A)
Microarray data analyses of the association between FOXC1 and
ER.alpha. expression in human breast cancers. FOXC1 mRNA levels in
breast cancer are shown in box plots. The student's t test was
conducted using the Oncomine software. Results from six
representative data sets [(Ginestier et al., 2006; Lu et al., 2008;
Richardson et al., 2006; Sorlie et al., 2001; Zhao et al., 2004)
and the Oncology-Breast Samples Project database (Bittnet et al.)
of the International Genomics Consortium (IGC) at
https://expo.intgen.org/expo/public] are presented. (B) Expression
of FOXC1 in ER.alpha.-positive or -negative human breast cancer
cell lines is shown by immunoblotting.
[0040] FIG. 19 shows FOXC1 mRNA levels in human breast cancer
tissues shown in box plots. Microarray data analyses of the
association between FOXC1 and ER.alpha. expression in human breast
cancers are shown for (A) the Pollock et al. data set; (B) the
Perou et al. data set; (C) the Sorlie et al. data set; and (D) the
Schuetet al. data set. FOXC1 expression is shown to be negatively
associated with ER.alpha. expression in breast cancer. The
student's t test was conducted using the Oncomine software.
[0041] FIG. 20 illustrates that FOXC1 downregulates ER.alpha.
expression. (A) FOXC1 and ER.alpha. mRNA levels in vector or FOXC1
overexpressing MCF-7 cells were measured by RT-PCR (left) and real
time RT-PCR (middle and right). (B) Protein levels of FOXC1,
ER.alpha., and ER.alpha.-regulated genes PR and IRS-1 in vector- or
FOXC1-overexpressing MCF-7 cells were measured by immunoblotting.
(C) FOXC1 was transiently transfected into MCF-7 cells.
Immunofluorescence staining of FOXC1 (green) and ER.alpha. (red)
was performed. The nuclear DNA (blue) was stained by DAPI.
Magnification: .times.400. (D) MCF-7 cells were transiently
transfected with the ERE-luc reporter construct and the FOXC1
construct or the control vector. Cells were treated with 10.sup.-8
M 17.beta.-estradiol (E2) for 24 h, and were then lysed. Luciferase
activity was measured and normalized to .beta.-galactosidase
activity. Data represent mean.+-.SD of three independent
experiments.
[0042] FIG. 21 is an immunoblot illustrating that FOXC1
downregulates ER.alpha. expression. FOXC1 was stably transfected
into T47D breast cancer cells. Protein levels of FOXC1, ER.alpha.,
and ER.alpha.-regulated genes PR and IRS-1 in vector-or
FOXC1-overexpressing T47D cells were measured by
immunoblotting.
[0043] FIG. 22 shows line (A) and bar (B-C) graphs illustrating
that FOXC1 reduces the sensitivity to estrogen and antiestrogen in
breast cancer cells. (A) Proliferation of FOXC1-overexpressing and
control MCF-7 cells in serum-free medium was measured by MTT
assays. (B) FOXC1-overexpressing and control MCF-7 cells were
serum-starved for 24 h, and then treated with 10.sup.-8 M E2 for
the indicated time periods. Cell proliferation was measured by MTT
assays and is presented as relative growth rates compared with the
vehicle control. (C) FOXC1-overexpressing and control MCF-7 cells
in regular medium were treated with 10.sup.-6 M tamoxifen for the
indicated time periods. Cell proliferation was measured by MTT
assays and is presented as relative growth rates vs. the vehicle
control.
[0044] FIG. 23 shows that FOXC1 induces NF-.kappa.B activity in
breast cancer cells. (A) Most significant canonical signaling
pathways identified in the three breast cancer subgroups from the
Richardson et al. dataset using Ingenuity Pathway Analysis software
is shown (Basal-like--(a), HER2--(b), Luminal--(c)). Genes from the
dataset that were associated with a canonical pathway in the
Ingenuity Pathways Knowledge Base were considered for the analysis.
Fischer's exact test was used to calculate a p-value determining
the probability that the association between the genes in a
particular subgroup and the canonical pathway is explained by
chance alone. Displayed canonical pathways appear in rank order of
their Impact Factor, the negative log of the Fischer's exact test
p-value. (B) Expression of NF-.kappa.B components in MCF-7 cells
overexpressing FOXC1 or the vector was examined by immunoblotting.
(C) Expression of p65 in 4T1 breast cancer cells stably transduced
with control or FOXC1 shRNA was examined by immunoblotting. (D)
Nuclear proteins were isolated from MCF-7 cells overexpressing
FOXC1 or the control vector, followed by immunoblotting of p65 and
the nuclear protein Lamin A/C. (E) Nuclear proteins were isolated
from MCF-7 cells overexpressing FOXC1 or the control vector. The
binding of p65, p50, and c-Rel to consensus DNA oligonucleotides
was assessed by ELISA. Data represent mean.+-.SD of three
independent experiments. (F) MCF-7 cells were transiently
transfected with NF-.kappa.B-luc, FOXC1, and a super-repressor
I.kappa.Ba. NF-.kappa.B activity was assessed by luciferase assays.
Each bar represents mean.+-.SD of three independent experiments.
(G) MCF-7 cells overexpressing FOXC1 or the vector were treated
with the IKK inhibitor BMS-345541 (5 .mu.M). Cell proliferation at
the indicated time points was measured by MTT assays and is
presented as relative growth rates compared with the vehicle
control.
[0045] FIG. 24 shows that NF-.kappa.B downregulates ER.alpha. in
breast cancer cells. (A) Expression of p65 and ER.alpha. in MCF-7
cells transfected with p65 or the vector for 48 h was examined by
immunoblotting. (B) Expression of ER.alpha., PR, and IRS-1 in MCF-7
cells treated with the IKK inhibitor BMS-345541 (5 .mu.M) for 24 h
was examined by immunoblotting. (C) MCF-7 cells were transiently
transfected with ERE-luc and ER.alpha. or p65, and then treated
with 10.sup.-8 M E2 for 24 h. ER activity was assessed by
luciferase assays. Each bar represents mean.+-.SD of three
independent experiments. (D) ChIP assays were performed as
described in Materials and Methods. Antibodies against p65 protein
were utilized to immunoprecipitate p65-DNA complexes. The input
control was 1% of the protein-chromatin supernatant subjected to
ChIP assays. The amplified ER.alpha. promoter region is -420/-280
(right).
[0046] FIG. 25 shows that NF-.kappa.B downregulates ER.alpha. in
breast cancer cells. Expression of ER.alpha. in MCF-7 cells
transfected with p65 or the vector for 48 h was examined by
real-time RT-PCR. Data represent mean.+-.SD of three independent
experiments.*, P<0.05 vs the vector control.
[0047] FIG. 26 shows representative immunostaining profiles of
CK5/6, CK14 and FOXC1 in FFPE breast cancer specimens according to
molecular subtype.
DETAILED DESCRIPTION
[0048] A method for classifying a tumor using a theranostic
biomarker with independent prognostic significance is provided
herein. A theranostic biomarker provides information relevant to
diagnosis, prognosis and treatment of cancer in a subject. Although
the present disclosure focuses on methods related to breast cancer
in humans, the methods described herein may be applied to any
cancer having one or more biomarkers with independent prognostic
significance in any subject susceptible to developing breast
cancer.
[0049] The term "theranostic biomarker" or a "theranostic
classification" as used herein means a particular biomarker or
classification that, in addition to providing significant
diagnostic and prognostic information, also provides information
useful in optimizing treatment of a subject having a disease such
as cancer. The embodiments described herein provide a theranostic
approach to classifying, diagnosing, prognosing and treating
cancer. In practical terms, this means that a theranostic biomarker
or theranostic classification can identify which subjects and which
tumors are most suited to a particular therapy, and also provides
feedback on the efficacy of a drug in order to demonstrate or
determine how well a drug should work or does work to optimize
therapy or therapy regimens. It can also identify which subjects
are resistant to particular therapy or therapy regimens.
[0050] In one embodiment, the theranostic biomarker may be specific
to a disease, such as breast cancer, or may be a general disease
biomarker. In one embodiment, the theranostic biomarker is FOXC1.
FOXC1 may be used as an independent theranostic biomarker, or may
be used in conjunction with other molecular biomarkers that are
relevant to a particular type of tumor or cancer. In one
embodiment, FOXC1 may be used alone or in conjunction with estrogen
receptor (ER), progesterone receptor (PR) and human epidermal
growth factor receptor 2 (HER2; also known as ErBb2mer-2/Neu)
status for use in a method for theranostic classification,
diagnosis, prognosis and treatment breast cancer and its subtypes.
In some embodiments, such methods are useful in distinguishing
between basal-like breast cancer subtypes, including hybrid
basal-like breast cancer subtypes that exhibit both basal-like
breast cancer characteristics and one or more characteristics of
another subtype such as luminal or HER2-enriched.
[0051] In one embodiment, the methods described herein include
providing or obtaining a tumor tissue sample. The tumor tissue
sample may be a fresh frozen tumor sample, a formalin-fixed
paraffin-embedded (FFPE) sample, a primary cell culture, or any
other suitable tissue for determining an expression level of a
biomaker. In one embodiment, the tumor tissue sample is a breast
cancer tumor tissue sample.
[0052] In some embodiments, an expression level of a theranostic
biomarker such as FOXC1 in a tumor tissue sample may be determined
by qualitative or quantitative methods such as immunohistochemistry
(IHC) or immunocytochemistry (ICC), non-quantitative or
quantitative reverse transcription polymerase chain reaction
(RT-PCR or qRT-PCR), protein or cDNA microarray or by a
QuantiGene.RTM. assay (Panomics). The expression level may be a
measurement of mRNA expression or protein expression. Data thus
derived may be used to develop a cutoff expression level or a
numerical prognostic index FOXC1 Score.TM. to aid in the clinical
prognostic stratification of specific subsets of patients with
breast cancer (and/or other cancers including but not limited to,
melanoma, neuroendocrine tumors, brain tumors such as glioblastoma
multiforme, astrocytoma and other brain cancers, renal cell cancer,
sarcomas (such as synovial sarcoma) and leukemia. The numerical
prognostic index FOXC1 Score.TM. may be calculated from a standard
curve as generated by plotting qRT-PCR values of FOXC1 mRNA
expression against a specific clinical outcome measure such as
overall survival (OS), breast-cancer specific survival, recurrence
free survival, matastasis-free survival, other suitable prognostic
or outcome measures. The numerical prognostic index FOXC1 Score.TM.
may be used for determining a subject's prognosis and may also be
used for clinical management purposes for tracking the efficacy or
optimizing the efficacy of one or more therapy regimens.
[0053] Breast Cancer Subtype Molecular Classification
[0054] Molecular classification of breast cancer has identified
specific subtypes, often called "intrinsic" subtypes, with clinical
and biological implications, including an intrinsic luminal
subtype, an intrinsic HER2-enriched subtype (also referred to as
the HER2.sup.+ or ER.sup.-/HER2.sup.+ subtype) and an intrinsic
basal-like breast cancer (BLBC) subtype. (Perou et al. 2000).
Identification of the intrinsic subtypes has typically been
accomplished by a combination of methods, including (1)
histopathological detection, (2) ER, PR and HER2 expression status
and (3) detection of characteristic cellular markers.
[0055] Basal-like breast cancer, which expresses genes
characteristic of basal epithelial cells in the normal mammary
gland, comprises up to 15%-25% of all breast cancers (Kreike et al.
2007) and is associated with the worst prognosis of all breast
cancer types. BLBCs underexpress estrogen receptor (ER.sup.-),
progesterone receptor (PR.sup.-), and human epidermal growth factor
receptor 2 (HER2.sup.-) and encompass 60% to 90% of so-called
"triple-negative" (ER.sup.-/PR.sup.-/HER2.sup.-) breast cancers.
Although most basal-like breast cancers are often referred to as
triple-negative based on the expression status of ER, PR and HER2,
not all basal-like breast cancers are triple negative. Thus, the
intrinsic basal-like breast cancer subtype may be further
subdivided into at least three distinct subtypes described herein
as "hybrid" basal-like breast cancer subtypes. In addition to a
hybrid triple-negative subtype, the hybrid basal-like breast cancer
subtypes have profiles that resemble both basal-like breast cancer
and at least one other breast cancer molecular subtype. For
example, hybrid basal-like subtypes can include a hybrid
basal-like/HER2.sup.+ subtype that has a receptor profile of
ER.sup.-/PR.sup.-/HER.sup.+, a hybrid basal-like/luminal subtype
that has a receptor profile of ER.sup.+/PR.sup.-or +/HER.sup.-or +,
and a hybrid basal-like/triple negative subtype that has a receptor
profile of ER.sup.-/PR.sup.-/HER.sup.-. The existence and
significance of these hybrid basal-like subtypes has not previously
been recognized, but because they represent some of the most
aggressive and resistant to treatment subtypes of breast cancer,
the methods described herein are important to improving the
diagnosis, prognosis and treatment of this disease. The term
"basal-like breast cancers," "basal-like subtypes," basal-like
tumors," "BLBCs" or the like as used herein is meant to encompass
all cancers and tumors that exhibit characteristics of the BLBC
subtype, including the intrinsic BLBC subtype, the hybrid
triple-negative BLBC subtype, and any other hybrid basal-like
subtypes described herein that may display markers that are
associated with the luminal, HER+ or other previously classified
subtype.
[0056] The intrinsic HER2-enriched subtype (also described as the
HER.sup.2+ or ER.sup.-/HER.sup.2+ subtype) is characterized by
underexpression of the hormone receptors ER and PR and
overexpression of HER2 (ER.sup.-/PR.sup.-/HER2.sup.+). The
HER2-enriched subtype is associated with a poor prognosis.
[0057] The intrinsic luminal breast cancer subtype is characterized
by expression or overexpression of ER and/or PR (ER.sup.+ and/or
PR.sup.+). The luminal subtype can be further subdivided based on
HER2 status into the luminal A subtype, which is additionally
characterized by underexpression of HER2
(ER.sup.+/PR.sup.+o-/HER.sup.-), and luminal B subtype, which is
additionally characterized by overexpression of HER2
(ER.sup.+/PR.sup.+or -/HER.sup.+). Intrinsic luminal subtypes are
often considered to be the most treatable breast cancer subtype and
are associated with the best prognosis.
[0058] Whereas ER and HER2 guide treatment of luminal and HER2
breast cancers, respectively, chemotherapy remains the only
modality of systemic therapy for BLBC. Preferentially affecting
younger women, particularly African American women, BLBCs are
associated with high histologic grade, aggressive clinical
behavior, and a high rate of metastasis to the brain and lung
(Carey et al. 2006). Unlike other breast cancer subtypes, there
seems to be no correlation between tumor size and lymph node
metastasis in BLBCs (Dent et al. 2007). Better understanding of the
signaling pathways, biologic basis, and molecular mechanisms of
basal-like, triple-negative breast cancer and other hybrid
basal-like subtypes described above allows identification of
accurate biomarkers for early diagnosis, prognosis, and targeted
therapy.
[0059] BLBCs are associated with expression of basal cytokeratins
(CK5/6, CK14, and CK17), epidermal growth factor receptor (EGFR),
c-kit, and p53 and associated with the absence of ER, PR, and HER2
expression. With a large variety of associated genes, BLBCs have
been defined differently in different studies using a set of
diagnostic markers. For example, Nielsen et al. defined BLBC on the
basis of negative ER and negative HER2 expression in addition to
positive basal cytokeratin, EGFR, and/or c-kit expression (Nielsen
et al. 2004). On the other hand, other groups have defined BLBC on
the basis of on a combination of negative ER, and negative HER2
expression and positive CK5, P-cadherin, and p63 expression
(Elsheikh et al. 2008) or positive vimentin, EGFR, and CK5/6
expression (Livasy et al. 2006). These different technical
approaches in combination with widely varying patient cohorts may
explain the inconsistent experimental results for these
markers.
[0060] Identification of the basal-like subtype using
immunohistochemistry (IHC) for detecting hormone receptors alone is
less desirable than detecting a theranostic biomarker, because
identification is based on the absence of IHC staining for estrogen
receptor (ER), progesterone receptor (PR), and human epidermal
growth factor receptor 2 (HER2) rather than the presence of a
specific tumor marker or markers. Its diagnosis is more one of
exclusion rather than inclusion. Basal-like breast cancer is often
synonymously referred to as "triple negative" (i.e.,
ER.sup.-/PR.sup.-/HER2.sup.-), however, not all triple negative
breast cancers are basal-like, and not all basal-like breast
cancers are triple negative. Although other molecular markers have
been associated with basal-like breast cancer as described above,
such markers are not exclusive to this basal-like breast cancer and
are therefore are not suitable for use as stand-alone markers. The
best hope for a realistic, potentially objective, and convenient
method to identify basal-like cancers in clinical practices would
be through the positive detection of a definitive molecular marker
or markers. Identification of FOXC1 as a dominant regulator of the
basal-like phenotype may provide a pragmatic approach to
distinguish this subgroup of breast cancer in clinical diagnosis,
ultimately resulting in improved survival.
[0061] FOXC1 as a Biomarker for Basal-like Breast Cancer
[0062] As described in the examples below, specific biomarkers for
BLBC were identified and systemically analyzed using a 306-member
intrinsic gene set (IGS) (Hu et al. 2006) in addition to other
reported individual markers for BLBC using multiple microarray data
sets. Degree of correlation 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. Therefore, diagnostic and prognostic
significance of FOXC1 was assessed and the role of FOXC1 in
regulating cellular functions in breast cancer was further
characterized.
[0063] Forkhead box transcription factors, including Forkhead box
C1 (FOXC1, also known as forkhead-like 7 (FKHL7)), are
transcription factors characterized by a common 100-amino acid
winged-helix DNA-binding domain termed the forkhead box domain, and
play important roles in regulating the expression of genes involved
in cell growth, survival, differentiation embryonic mesoderm
development, migration, and longevity (Nishimura et al., 1998). The
FOXC1/FKHL7 gene and protein sequences are known, and can be found
in GenBank (Accession Nos. AR140209 (complete sequence; SEQ ID
NO:12), AR140210 (coding sequence; SEQ ID NO:13) and AAE63616
(amino acid sequence; SEQ ID NO14), the sequences of which are
incorporated by reference in their entirety as if fully set forth
herein). As a result of the studies described herein, it has been
determined that FOXC1 expression in human breast cancer, both at
the mRNA and at the protein level, occurs consistently and
exclusively in basal-like breast cancers. Furthermore, in a
head-to-head comparison with other suggested biomarkers of
basal-like breast cancer and as shown by statistically significant
in both univariate as well as multivariate analyses described in
the examples below, FOXC1 has emerged as the most indicative and
the most characteristic biomarker of BLBCs, in its ability to
diagnose, prognose and treat BLBC.
[0064] One important feature of the above results was the exclusive
nature of the association between FOXC1 and basal-like breast
cancers: its expression is elevated only in basal-like molecular
subtypes of breast cancers.
[0065] As mentioned above, while many genes are described to be
characteristic biomarkers of certain cancer types, and many others
are described to be of functional importance to the survival and
maintenance of the malignant phenotype, very few are demonstrated
to have robust prognostic significance. This is because very few
are critical by themselves and instead are part of extremely large
and complex networks of biomolecules whose overall function cannot
be determined unless the molecules which are most central and
pivotal in the network are identified. FOXC1 has been demonstrated
to be of extremely high prognostic significance, being predictive
of the high mortality and metastasis rate specifically associated
with basal-like breast cancers.
[0066] Both basal-like triple-negative breast cancers as well as
hybrid basal-like breast cancers (HER2 and luminal) have a high
rate of metastasis to the brain, a devastating complication of this
dreaded disease. The studies described herein show that a 30-member
gene signature associated with FOXC1 is predictive of the brain
specific metastases observed in the above two subtypes of breast
cancer.
[0067] The clinical significance of FOXC1 expression is not
restricted to breast cancer but may extend to other cancers,
including but not limited to, melanoma, neuroendocrine tumors,
brain tumors (such as glioblastoma multiforme, or astrocytoma),
renal cell cancer, sarcomas (such as synovial sarcoma), and
leukemia. FOXC1 expression has been shown to characteristically and
exclusively define biologically and clinically aggressive subsets
in such cancers and can be used both as a diagnostic as well as
prognostic biomarker for these specific cancer types. Furthermore,
similar to basal-like breast cancer, FOXC1 is a suitable
therapeutic target for these specific cancer types.
[0068] The above described findings have clear and important
implications for personalized medicine and personalized cancer care
as detection of FOXC1 status of the described specific subsets of
patients with breast cancer (and/or other cancers like gastric
cancer and colon cancer) enables more tailored and specific
therapeutic interventions with a greater likelihood of arresting
disease progression, extending life expectancy or even achieving a
cure.
[0069] In some embodiments, a method of use for a theranostic
biomarker such as FOXC1 comprises an algorithm for its potential
clinical use as a diagnostic tool. While FOXC1 may be used as an
independent biomarker, it may also be used alongside other
biomarkers such as HER2, ER and PR. For example, in triple-negative
breast cancer, the algorithm may include the following steps.
First, a patient who has either mammographic or breast
MRI--detected abnormality or findings on a clinical examination is
designated as suspicious for breast cancer. Next, the patient would
undergo a FNNCore biopsy/Excisional biopsy to obtain a tumor
tissue. Next, routine pathologic examination of the above-obtained
tumor tissue establishes diagnosis of breast cancer. The tumor
tissue would then be subjected to immunohistochemical (IHC)
staining for ER, PR and HER2. Patients that are found to be triple
negative (i.e. ER.sup.-/PR.sup.-/HER2.sup.-) would have their tumor
tissue further tested by IHC for FOXC1. Next, patients that are
found to be FOXC1 positive can thus be definitively diagnosed to
have basal-like triple negative breast cancer.
[0070] In another embodiment a theranostic biomarker such as FOXC1
is used as a prognostic tool. FOXC1 may be used to predict the
prognosis of factors including, but not limited to, overall
survival, recurrence-free survival, the propensity of developing a
distant metastasis or the time to develop a distant metastasis
(such as brain metastasis), or a propensity for resistance to a
targeted cancer therapy regimen. The term "propensity for
resistance" to a targeted cancer therapy regimen as used herein may
be a predictor of resistance or a predictor of a decreased efficacy
(i.e., therapy is less effective from the start of treatment) of a
targeted cancer therapy regimen in a cancer patient. A high level
of FOXC1 (either protein or RNA) predicts a poor prognosis of such
factors, (i.e., decreased overall survival, decreased disease
specific survival, decreased recurrence-free survival, increased
rate of loco-regional and for distant metastasis) as compared with
low FOXC1 levels in specific subsets of patients with breast cancer
(and/or other cancers including but not limited to, melanoma,
neuroendocrine tumors, brain tumors--such as glioblastoma
multiforme, astrocytoma, renal cell cancer, sarcomas--such as
synovial sarcoma, and leukemia).
[0071] In one embodiment, FOXC1's use as a prognostic tool includes
an algorithm. For example, the algorithm may include the following
steps, using triple-negative breast cancer as an example. First, a
subject whose samples are qualitatively FOXC1 positive based on IHC
have samples sent for further quantitative analysis for FOXC1 level
using an RT-PCR or other quantitative technique such as a
QuantiGene.RTM. assay (Panomics). Based on the quantitative value
of FOXC1 expression thus obtained, a numerical Prognostic Index
FOXC1 Score.TM. will be calculated for the individual patient which
will help determine patient-specific estimates of overall survival,
recurrence free survival, time to distant metastasis and type of
metastasis associated with basal-like triple-negative breast
cancer. This method makes personalized medical care possible for
BLBC patients.
[0072] FOXC1 Represses Estrogen Receptor-.alpha. Expression in
Human Breast cancer Cells by Increasing Nuclear Factor-.kappa.B
(NF-.kappa.B) Signaling
[0073] The sex steroid hormone estrogen plays important roles in
the development of normal mammary glands and breast cancer
(Dhasarathy et al., 2007). Most established effects of estrogen are
mediated through its direct binding to two nuclear receptors,
estrogen receptor (ER)-.alpha. and -.beta. (Couse and Korach, 1999;
Kuiper et al., 1997). Both receptors are transcription factors that
induce the expression of many breast cancer-related genes. Although
ER.beta. is expressed in breast cancer, its role in tumor
progression is not clear (Fuqua et al., 2003). On the other hand,
the role of ER.alpha. in human breast cancer is well-established.
More than 60% of human breast cancers are ER.alpha. positive (Keen
and Davidson, 2003). It is a prognostic factor for breast cancer
and correlates with a higher degree of tumor differentiation and
increased disease-free survival (Osborne, 1998). Thus ER.alpha.
expression defines a subgroup of breast cancer patients who, in
general, have a more favorable prognosis than patients with
ER.alpha.-negative tumors (Zhao et al., 2008). ER.alpha. is also a
target for antiestrogen therapy and a predictive marker for
response to the therapy (Park and Jordan, 2002).
[0074] There is tremendous interest in understanding the mechanisms
whereby ER.alpha. expression and signaling is modulated in breast
cancer and in exploiting this knowledge to develop and improve
therapeutic interventions targeting ER.alpha.. Although several
transcription factors or signaling proteins have been identified as
ER.alpha. regulators, the cellular and molecular events that
regulate ER.alpha. expression in tumors are not well understood as
yet. In addition, the clinical relevance and biological
significance of these regulations are still under investigation. It
was found that p53 binds to the ER.alpha. promoter and positively
regulates the transcription of ER.alpha. in breast cancer cells
(Shirley et al., 2009). In contrast, another study showed that p53
activation decreases the transcriptional activity of ER.alpha. by
elevating the Kruppel-like factor 4 transcription factor, which can
interfere with the DNA-binding function of ER.alpha. (Akaogi et
al., 2009). Similarly, the BRCA1 tumor suppressor gene has been
found to activate or inhibit ER.alpha. expression in different
studies (Nosey et al., 2007; Rosen et al., 2005). The transcription
factor Oct-1 can also be recruited to the ER.alpha. promoter to
elicit ER.alpha. transcription (Nosey et al., 2007).
[0075] In breast cancer cell lines, expression of ER.alpha. is
associated with levels of active forkhead box O protein 3a (FOXO3a)
(Guo and Sonenshein, 2004). Increased FOXO3a expression induces
ER.alpha. transcription and protein levels. FOXO3a can bind to two
conserved forkhead binding sites in the ER.alpha. promoter. Thus
FOXO3a may represent an important intracellular mediator of
ER.alpha. expression (Guo and Sonenshein, 2004). In support of this
study, Belguise et al. showed that PKCq is elevated in
ER.alpha.-negative breast cancers, activates Akt and thereby
inactivates FOXO3a, leading to decreased synthesis of ER.alpha.
(Belguise and Sonenshein, 2007). It is also well-documented that
hyperactivation of MAPK induces loss of ER.alpha. expression in
breast cancer cells (Oh et al., 2001). Both Akt and MAPK may be
implicated in the downregulation of ER.alpha. by EGFR/HER-2, which
may give rise to an inverse correlation between EGFR/HER-2 and
ER.alpha. status in breast cancers (Oh et al., 2001; Saceda et al.,
1996). Most recently, a G protein-coupled receptor Adenosine A1
receptor has been reported to upregulate ER.alpha. expression (Lin
et al.). Furthermore, ER.alpha. expression can also be regulated
through epigenetic modification, e.g. hypermethylation at its
promoter, which has been reported to be responsible for the loss of
ER.alpha. in some breast cancer cells (Yoshida et al., 2000).
[0076] As described by the studies herein, forkhead box
transcription factor FOXC1 has been identified as an important
marker for human basal-like breast cancer, which lacks or
under-expresses estrogen receptor-.alpha. (ER.alpha.). Further, as
discussed in detail below, FOXC1 expression was shown to
consistently and inversely correlate with ER.alpha. expression by
analyzing multiple cDNA microarray data sets of human breast
cancer. Overexpression of FOXC1 in ER.alpha.-positive breast cancer
cells downregulated ER.alpha. mRNA and protein levels, and reduced
cellular responses to estradiol and tamoxifen treatment. FOXC1
overexpression caused an increase in levels of p65 protein, thereby
eliciting NF-.kappa.B-mediated suppression of ER.alpha..
Pharmacologic inhibition of NF-.kappa.B in FOXC1-overexpressing
MCF-7 breast cancer cells diminished these effects of FOXC1. Taken
together, these results reveal a FOXC1-driven mechanism that
explains the loss or low expression of ER.alpha. in basal-like
breast cancer and provide a paradigm for studying the regulation of
ER.alpha. during breast cancer progression.
[0077] FOXC1 as a Therapeutic Target for Basal-like Breast
Cancer
[0078] The studies described herein show that FOXC1 plays an
important role in initiating and maintaining the aggressive
capacity for cellular proliferation, invasion and migration that is
typical of basal-like breast cancers. These are well accepted
precursor attributes that are necessary for and associated with
metastasis to distant organs, a clinical feature which is predicted
by a patient's FOXC1 status.
[0079] Studies in which FOXC1 expression is targeted and knocked
down dramatically reduces the above aggressive features of cancer
cells. This demonstrates the utility of FOXC1 as a therapeutic drug
target specifically for basal-like breast cancers.
[0080] While the clinical significance of the hybrid basal-like
subtypes described above has not previously been recognized, these
subtypes are typically resistant to targeted receptor therapy, even
though they express the target receptor. For example, the hybrid
basal-like/HER2.sup.+ subtype is typically intrinsically resistant
to HER2.sup.+ targeted therpies including, but not limited to,
anti-HER2 antibodies (e.g., trastuzumab (Herceptin.RTM.),
pertuzumab and ertumaxomab) and tyrosine kinase inhibitors (e.g.,
lapatinib)), despite being HER2 positive. Similarly, the hybrid
basal-like/luminal subtype is typically intrinsically resistant to
hormone receptor targeted therapies including, but not limited to,
selective estrogen receptor modulators (SERMS) (e.g., tamoxifen),
and other therapies such as aromatase inhibitors (e.g., anastozole
(Arimidex.RTM.), exemestane (Aromasin.RTM.) and letrozole
(Femara.RTM.)) and anti-estrogens (e.g., toremifene citrate
(Fareston.RTM.), This resistance to or decrease in efficacy to
targeted receptor therapy is indicated by FOXC1. Thus, FOXC1
positive status may be used as a predictive biomarker of resistance
to or decrease in efficacy of biologic therapy attempted with
trastuzumab (Herceptin.RTM.) or tamoxifen in patients with hybrid
basal-like breast cancers. Administration of targeted therapy
directed against FOXC1 in hybrid basal-like/HER2.sup.+ subtype
tumors and hybrid basal-like/luminal subtype tumors should restore
therapeutic sensitivity to trastuzumab (Herceptin.RTM.) and
tamoxifen, respectively.
[0081] Hybrid basal-like subtype tumors are even more aggressive in
their biology and clinical characteristics than either the
molecular subtype (HER2.sup.+ or luminal) or the basal-like subtype
alone. Hence any and all therapeutic efforts in this group should
include FOXC1 targeted therapy as well as targeted therapy from the
earliest possible time after diagnosis.
[0082] Validated as a prognostic biomarker, FOXC1 status can be
utilized in clinical decision making with respect to
recommendations for offering standard adjuvant chemotherapy,
enrollment in adjuvant chemotherapy clinical trials, offering
neoadjuvant chemotherapy, and enrollment in neoadjuvant
chemotherapy clinical trials, to patients with basal-like breast
cancer. FOXC1 status may also be utilized in clinical decision
making with respect to treatment recommendations for a triple
negative-diagnosed patient based on a determination that the
patient has a BLBC subtype that is resistant to targeted or other
treatments or treatment regimens. For example, a patient diagnosed
as triple negative and FOXC1.sup.+ is likely to be resistant to
most targeted therapies and/or chemotherapy, and may therefore
decide to forego treatments or treatment regimens in favor of
living the rest of their life without the negative effects that are
often associated with said treatments. Alternatively, a FOXC1
inhibitor or other FOXC1 targeted therapy may be used in
conjunction with adjuvant and neoadjuvant chemotherapy
regimens.
[0083] The pharmaceutical composition may include, but is not
limited to, an FKBP52 inhibitor, a CD147 inhibitor, and a
pharmaceutically acceptable carrier.
[0084] In one embodiment, a method for treating cancer may include
administering a pharmaceutical composition that includes a
pharmaceutically acceptable carrier and a therapeutically effective
amount of a substance that targets and inhibits FOXC1 expression or
activity (a FOXC1 inhibitor) for the targeted biologic therapy of
basal-like/triple negative breast cancer. In another embodiment,
the pharmaceutical composition may also include a therapeutically
effective amount of a substance that targets a receptor for the
targeted biologic therapy of other hybrid basal-like breast
cancers. In one embodiment, the substance that targets a receptor
may include, but is not limited to, ER (for targeting the hybrid
basal-like/luminal subtype) or HER2 (for targeting the hybrid
basal-like/HER2 subtype).
[0085] In one embodiment, the FOXC1 inhibitor may include any
suitable substance able to target intracellular proteins or nucleic
acid molecules alone or in combination with an appropriate carrier
or vehicle, including, but not limited to, an antibody or
functional fragment thereof, (e.g., Fab', F(ab').sub.2, Fab, Fv,
rIgG, and scFv fragments and genetically engineered or otherwise
modified forms of immunoglobulins such as intrabodies and chimeric
antibodies), small molecule inhibitors of the FOXC1 protein,
chimeric proteins or peptides, gene therapy for inhibition of FOXC1
transcription, or an RNA interference (RNAi)-related molecule or
morpholino molecule able to inhibit FOXC1 gene expression and/or
translation. In one embodiment the FOXC1 inhibitor is an
RNAi-related molecule such as an siRNA or an shRNA for inhibition
of FOXC1 translation. An RNA interference (RNAi) molecule is a
small nucleic acid molecule, such as a short interfering RNA
(siRNA), a double-stranded RNA (dsRNA), a micro-RNA (miRNA), or a
short hairpin RNA (shRNA) molecule, that complementarily binds to a
portion of a target gene or mRNA so as to provide for decreased
levels of expression of the target.
[0086] The pharmaceutical compositions of the subject invention can
be formulated according to known methods for preparing
pharmaceutically useful compositions. Furthermore, as used herein,
the phrase "pharmaceutically acceptable carrier" means any of the
standard pharmaceutically acceptable carriers. The pharmaceutically
acceptable carrier can include diluents, adjuvants, and vehicles,
as well as implant carriers, and inert, non-toxic solid or liquid
fillers, diluents, or encapsulating material that does not react
with the active ingredients of the invention. Examples include, but
are not limited to, phosphate buffered saline, physiological
saline, water, and emulsions, such as oil/water emulsions. The
carrier can be a solvent or dispersing medium containing, for
example, ethanol, polyol (for example, glycerol, propylene glycol,
liquid polyethylene glycol, and the like), suitable mixtures
thereof, and vegetable oils. In one embodiment, the
pharmaceutically acceptable carrier is a PEGylated immunoliposome
for encapsulating the RNAi-related molecule. The PEGylated
immunoliposomes or other carrier or delivery vehicle may be
specifically targeted to basal-like tumor cells or specific hybrid
basal-like subtype tumor cells by conjugating recombinant human
and/or chimeric monoclonal antibodies or functional fragments
thereof to the liposomal membrane which are specific for cell
surface protein and/or carbohydrate and/or glycoprotein markers
specific to the basal-like subtype that is targeted. Such markers
that may be targteted include, but are not limited to, CD109,
HMW-MAA, HER2, ER, CK5/6, EGFR, c-Kit and any other suitable marker
for targeting a desired tumor subtype.
[0087] Compositions containing pharmaceutically acceptable carriers
are described in a number of sources which are well known and
readily available to those skilled in the art. For example,
Remington: The Science and Practice of Pharmacy (Gerbino, P. P.
[2005] Philadelphia, Pa., Lippincott Williams & Wilkins, 21st
ed.) describes formulations that can be used in connection with the
subject invention. Formulations suitable for parenteral
administration include, for example, aqueous sterile injection
solutions, which may contain antioxidants, buffers, bacteriostats,
and solutes which render the formulation isotonic with the blood of
the intended recipient; and aqueous and nonaqueous sterile
suspensions which may include suspending agents and thickening
agents. The formulations may be presented in unit-dose or
multi-dose containers, for example sealed ampoules and vials, and
may be stored in a freeze dried (lyophilized) condition requiring
only the condition of the sterile liquid carrier, for example,
water for injections, prior to use. Extemporaneous injection
solutions and suspensions may be prepared from sterile powder,
granules, tablets, etc. It should be understood that in addition to
the ingredients particularly mentioned above, the formulations of
the subject invention can include other agents conventional in the
art having regard to the type of formulation in question.
[0088] The pharmaceutical composition described above is
administered and dosed in accordance with good medical practice,
taking into account the clinical condition of the individual
patient, the site and method of administration, scheduling of
administration, patient age, sex, body weight, and other factors
known to medical practitioners. The therapeutically effective
amount for purposes herein is thus determined by such
considerations as are known in the art. For example, an effective
amount of the pharmaceutical composition is that amount necessary
to provide a therapeutically effective decrease in FOXC1. The
amount of the pharmaceutical composition should be effective to
achieve improvement including but not limited to total prevention
and to improved survival rate or more rapid recovery, or
improvement or elimination of symptoms associated with the chronic
inflammatory conditions being treated and other indicators as are
selected as appropriate measures by those skilled in the art. In
accordance with the present invention, a suitable single dose size
is a dose that is capable of preventing or alleviating (reducing or
eliminating) a symptom in a patient when administered one or more
times over a suitable time period. One of skill in the art can
readily determine appropriate single dose sizes for systemic
administration based on the size of the patient and the route of
administration.
[0089] Having described the invention with reference to the
embodiments and illustrative examples, those in the art may
appreciate modifications to the invention as described and
illustrated that do not depart from the spirit and scope of the
invention as disclosed in the specification. The examples are set
forth to aid in understanding the invention but are not intended
to, and should not be construed to, limit its scope in any way. The
examples do not include detailed descriptions of conventional
methods. Such methods are well known to those of ordinary skill in
the art and are described in numerous publications. All references
cited above and below in the specification are incorporated by
reference in their entirety, as if fully set forth herein.
Example 1
FOXC1 is a Prognostic Biomarker with Functional Significance in
Basal-like Breast Cancer
[0090] Gene expression signatures for a basal-like breast cancer
(BLBC) subtype have been associated with poor clinical outcomes. As
described below, overexpression of the transcription factor FOXC1
is shown to be 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.
These findings identify FOXC1 as a theranostic 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.
[0091] Materials and Methods
[0092] Microarray Analysis. Publicly available datasets of human
breast cancer gene expression microarrays (Richardson et al. 2006;
Farmer et al. 2005; Hess et al. 2006; Ivshina et al. 2006; Miller
et al. 2005; van de Vijver et al. 2002; Herschkowitz et al. 2007;
Sorlie et al. 2003; Wang et al. 2005; Pawitan et al. 2005)
comprising of raw expression level data files and the ExpO Project
database of the International Genomics Consortium (IGC) at
https://expo.intgen.org were downloaded were analyzed using
Genespring GX 10.0 software (Agilent Technologies) (see Table 1
below). A total of 2,073 breast cancer patient samples were
analyzed. For cDNA arrays (3 of 11 data sets), the log.sub.2
normalized signal intensity values were directly imported into the
Genespring software platform, obtained from the respective public
web repository. For microarray raw data obtained from Affymetrix
arrays (8 of 11 data sets), signal intensities were obtained using
the Robust Multi-chip Averaging (RMA) algorithm to perform
background correction, normalization and summarization of
probe-level raw data. All values underwent baseline transformation
to median of all samples in a particular dataset on a (per
gene)/(per probe) set basis.
TABLE-US-00001 TABLE 1 Summary of analyzed microarray datasets.
Reference Sample Complete IHC Survival No. Array Name Platform
Technology Size Data Analysis ExpO Affymetrix U133 plus 2.0 250 - -
9 Richardson et al. Affymetrix U133 plus 2.0 47 - - 10 Farmer et
al. Affymetrix U133A 49 - - 11 Hess et al. Affymetrix U133A 133 + -
12 Ivshina et al. Affymetrix U133A 249 - - 13 Miller et al.
Affymetrix U133A 251 - - 14 van de Vijver et al. cDNA 295 - + 15
Herschkowitz et al. cDNA 232 - + 16 Sorlie et al. cDNA 122 - + 17
Wang et al. Affymetrix U133A 286 - + 18 Pawitan et al. Affymetrix
U133A 159 - +
[0093] All microarray datasets used in this study are from publicly
available databases, and such databases require that the gene
expression raw data, deposited by the original investigators, meet
stringent quality control criteria prior to acceptance.
Furthermore, each dataset has been earlier reported in the
literature and individual quality control measures are reported in
the original references. As such, in the present study, quality
control measures were taken to confirm prior established data
quality, rather than as an initial step to document data quality.
Array quality control was performed using 3D Principal Component
Analysis (PCA) plots, Internal Controls comprising of 3'/5' ratios
for a set of specific housekeeping gene probe sets, and
Hybridization Controls. A 3'/5' ratio of greater than 3 was
considered to be unacceptable (representative of either degraded
starting RNA or problem with the cDNA synthesis reaction). The
signal intensities of pre-mixed hybridization control transcripts
added to the hybridization mix in known staggered concentrations
should increase as expected with the known staggered
concentrations. Deviation from the expected intensity profile of
these controls, as assessed by visual inspection of Hybridization
Control plots, was considered to be unacceptable (representative of
a problem either with the hybridization or washing process). Based
on these criteria, only one array (from the Richardson et al.
dataset) among a total of 2,073 examined arrays was removed. The
PCA scores of each array were plotted in 3D in order to examine the
clustering pattern of samples. Three major clusters were observed
in each dataset, consistent with the expected biologic variation in
this population resulting in segregation into the three molecular
subtypes--luminal, HER2 and basal-like. Probes from the spotted
arrays were filtered based on flag values. Otherwise they were
filtered based on signal intensity values so that values between
20.0 and 100.0 percentiles in a given dataset were retained.
[0094] For identification of the molecular subtypes, we employed
the commonly used 306-member Intrinsic Gene Set (IGS) (Hu et al.
2006). Only 293 genes of the original 306-gene panel were
represented on the microarray platform of our test dataset that was
selected based on its inclusion of normal breast tissue samples
(Richardson et al 2006). We subjected all datasets to a
hierarchical clustering algorithm employing a Pearson uncentered
similarity metric and the average linkage rule based on the
293-gene IGS. Datasets were then clustered into luminal A/B, HER2,
and basal-like subtypes based on IGS. In the Richardson et al.
dataset, 12 samples were excluded as they were derived from normal
organoid preparations and not normal breast tissue, 4 BRCA positive
samples were excluded to reduce bias, 1 sample was excluded for not
meeting quality control standards and 1 sample classified by the
authors as basal-like clustered with the luminal subtype and was
thus excluded from the analysis.
[0095] To determine the correlation between FOXC1 and
triple-negative status, we searched for publicly available datasets
that contained complete ER, PR, and HER2 expression profiles of
each breast cancer specimen based on immunohistochemical analysis.
Only one such dataset (Hess et al.) was identified (Hess et al.
2006).
[0096] Average relative mRNA levels (mean log.sub.2 signal
intensity) for each IGS gene and for reported markers of BLBC in
the literature (.alpha.B-crystallin (Moyano et al. 2006), moesin
(Charafe-Jauffret et al. 2007), CD109 (Hasegawa et al. 2008),
p-Cadherin, EGFR (Nielsen et al. 2004), CK5 (Nielsen et al. 2004;
Korsching et al. 2008), CK14 (Korsching et al. 2008), CK17
(Korsching et al. 2008), c-Kit (Nielsen et al. 2004), ITGB4 (Lu et
al. 2008), and FOXC2 (Mani et al. 2007)) were determined according
to molecular subtype. Expression values for some genes were not
normally distributed for which reason we employed nonparametric
analysis (Mann-Whitney Test) in comparing Basal-like group vs.
pooled non-Basal-like group expression values (log.sub.2 normalized
signal intensity) for each gene. All statistical analyses were
performed using SAS software (Version 9.1.3, SAS Institute, Cary,
N.C.). A stepwise logistic regression analysis was performed to
identify the gene most characteristic of the basal-like group. In
view of the small sample size of the Richardson et al. dataset
(with highly predictive covariates resulting in non-convergence),
Firth's modified logistic regression analysis used to reduce the
bias of maximum likelihood estimation in this array. Statistical
significance for each of these analyses was defined as P<0.05.
To maintain statistical power, each dataset was analyzed
independently as shown below in Tables 2-5.
TABLE-US-00002 TABLE 2 Statistical analysis of biomarker in
molecular subgroups classified by IGS in the Richardson et al.
breast cancer microarray dataset (2). Univariate Wilcoxon
Multivariate Normal Luminal HER2 Basal-like Rank Sum Test Logistic
Mean .+-. SD Mean .+-. SD Mean .+-. SD Mean .+-. SD (Basal-like vs.
Regression Gene (Median) (Median) (Median) (Median) Other) P-value
P-value.sub..dagger. FOXC1 -0.11 .+-. 0.73 (-0.23) -1.63 .+-. 0.38
(-1.60) -0.99 .+-. 0.71 (-1.03) 3.61 .+-. 0.75 (3.63) <0.0001
0.0006 CRYAB -1.87 .+-. 0.39 (1.87) -1.61 .+-. 1.11 (-1.66) -1.82
.+-. 0.78 (-1.93) 1.34 .+-. 1.30 (1.27) 0.001 NS KRT5 2.98 .+-.
0.38 (2.72) -1.17 .+-. 0.98 (-1.32) -1.35 .+-. 1.02 (-1.31) 0.81
.+-. 1.48 (1.14) NS NS KIT 3.08 .+-. 0.47 (3.07) -0.93 .+-. 0.88
(-1.03) -1.21 .+-. 0.76 (-1.16) 0.60 .+-. 1.22 (0.32) NS NS CDH3
1.21 .+-. 0.43 (1.35) -1.13 .+-. 0.66 (-1.40) -0.11 .+-. 0.92
(-0.20) 0.64 .+-. 1.24 (0.97) 0.036 NS MSN 0.18 .+-. 0.33 (0.15)
-0.32 .+-. 0.61 (-0.41) -0.57 .+-. 0.44 (-0.51) 0.60 .+-. 0.88
(0.70) 0.002 NS KRT17 2.84 .+-. 0.25 (2.81) -1.03 .+-. 0.80 (-1.05)
-0.79 .+-. 1.13 (-0.95) 0.92 .+-. 1.78 (1.05) NS NS EGFR 0.32 .+-.
0.40 (0.39) -0.50 .+-. 0.30 (-0.51) 0.02 .+-. 0.67 (0.19) 0.25 .+-.
0.55 (0.27) 0.044 NS KRT14 3.44 .+-. 0.51 (3.60) -1.30 .+-. 1.84
(-0.82) -2.15 .+-. 1.82 (-1.49) 0.69 .+-. 2.53 (0.59) NS NS CD109
-0.46 .+-. 0.62 (-0.25) -0.45 .+-. 0.91 (-0.19) -0.77 .+-. 1.02
(-1.08) 0.46 .+-. 0.98 (0.63) 0.004 NS ITGB4 0.40 .+-. 0.39 (0.34)
-0.32 .+-. 0.35 (-0.37) 0.24 .+-. 0.74 (0.01) 0.19 .+-. 0.87 (0.13)
NS NS FOXC2 0.09 .+-. 0.13 (0.07) -0.01 .+-. 0.20 (-0.03) -0.01
.+-. 0.16 (0.03) 0.04 .+-. 0.26 (0.01) NS NS Values in each
molecular subtype column are the mean .+-. SD of the log2
normalized signal intensity for the best representative cDNA probe
for that gene. NS, P > 0.05. .sub..dagger. Firth's modified
logistic regression analysis used to reduce the bias of maximum
likelihood estimation in this array (characterized by small sample
size with highly predictive covariates resulting in
non-convergence). Basal-like (yes = 1, no = 0) was used as a
dependent variable.
TABLE-US-00003 TABLE 3 Statistical analysis of biomarker in
molecular subgroups classified by IGS in the Ivshina et al. breast
cancer microarray dataset (14). Univariate Wilcoxon Multivariate
Luminal HER2 Basal-like Rank Sum Test Logistic Mean .+-. SD Mean
.+-. SD Mean .+-. SD (Basal-like vs. Regression Gene (Median)
(Median) (Median) Other) P-value P-value FOXC1 0.02 .+-. 0.27
(-0.01) 0.00 .+-. 0.28 (-0.05) 1.88 .+-. 0.71 (1.92) <0.0001
0.0033 CDH3 -0.06 .+-. 0.76 (-0.18) 0.90 .+-. 0.84 (0.73) 1.94 .+-.
0.92 (2.07) <0.0001 010199 CRYAB -0.03 .+-. 0.79 (-0.06) -0.30
.+-. 0.44 (-0.40) 1.94 .+-. 1.23 (2.20) <0.0001 NS EGFR -0.05
.+-. 0.94 (-0.17) 0.41 .+-. 0.86 (0.28) 1.20 .+-. 0.57 (1.15)
<0.0001 NS KRT17 0.13 .+-. 0.99 (-0.11) 0.51 .+-. 1.10 (0.09)
2.06 .+-. 1.54 (1.99) <0.0001 NS KRT5 0.09 .+-. 1.08 (-0.21)
0.12 .+-. 0.85 (0.00) 2.20 .+-. 1.34 (2.20) <0.0001 NS MSN .sup.
-0.10 .+-. 0.48 (-0.04) - 0.06 .+-. 0.35 (-0.12) 0.74 .+-. 0.42
(0.82) <0.0001 NS ITGB4 -0.03 .+-. 0.44 (-0.07) 0.28 .+-. 0.46
(0.35) 0.45 .+-. 0.65 (0.26) 0.0016 NS KIT 0.11 .+-. 1.00 (0.00)
-0.27 .+-. 0.87 (-0.46) 1.07 .+-. 1.45 (1.25) 0.0011 NS KRT14 -0.24
.+-. 1.96 (-0.15) -0.30 .+-. 1.47 (-0.57) 1.99 .+-. 2.08 (1.55)
0.0001 NS FOXC2 0.00 .+-. 0.17 (0.00) 0.05 .+-. 0.16 (0.05) 0.07
.+-. 0.26 (0.02) NS NS Values in each molecular subtype column are
the mean .+-. SD of the log2 normalized signal intensity for the
best representative cDNA probe for that gene. NS, P > 0.05. *
CD109 does not have any representative probes on this microarray
platform. In the multivariate logistic regression analysis,
dependent variable is basal-like.
TABLE-US-00004 TABLE 4 Statistical analysis of biomarker in
molecular subgroups classified by IGS in the Miller et al. breast
cancer microarray dataset (15). Univariate Wilcoxon Multivariate
Luminal HER2 Basal-like Rank Sum Test Logistic Mean .+-. SD Mean
.+-. SD Mean .+-. SD (Basal-like vs. Regression Gene (Median)
(Median) (Median) Other) P-value P-value FOXC1 0.02 .+-. 0.28
(-0.01) -0.04 .+-. 0.29 (-0.12) 1.86 .+-. 0.71 (1.90) <0.0001
0.0003 CDH3 -0.05 .+-. 0.76 (-0.16) 0.87 .+-. 0.94 (0.75) 1.95 .+-.
0.91 (2.09) <0.0001 0.0153 KRT17 0.13 .+-. 1.01 (-0.09) 0.48
.+-. 1.11 (0.08) 2.05 .+-. 1.54 (1.99) <0.0001 NS EGFR -0.04
.+-. 0.94 (-0.17) 0.39 .+-. 0.88 (0.08) 1.21 .+-. 0.57 (1.16)
<0.0001 NS MSN -0.09 .+-. 0.47 (-0.03) -0.13 .+-. 0.42 (-0.19)
0.74 .+-. 0.42 (0.82) <0.0001 NS CRYAB -0.01 .+-. 0.78 (-0.05)
-0.38 .+-. 0.48 (-0.41) 1.94 .+-. 1.24 (2.20) <0.0001 NS KRT5
0.10 .+-. 1.08 (-0.16) 0.07 .+-. 0.88 (0.00) 2.19 .+-. 1.33 (2.18)
<0.0001 NS KRT14 -0.26 .+-. 1.96 (-0.14) -0.40 .+-. 1.50 (-0.61)
1.94 .+-. 2.07 (1.50) <0.0001 NS ITGB4 -0.02 .+-. 0.45 (-0.07)
0.21 .+-. 0.46 (0.27) 0.45 .+-. 0.65 (0.26) 0.002 NS KIT 0.12 .+-.
1.01 (0.01) -0.27 .+-. 0.86 (-0.43) 1.07 .+-. 1.45 (1.25) 0.001 NS
FOXC2 0.00 .+-. 0.17 (0.00) 0.03 .+-. 0.18 (0.05) 0.06 .+-. 0.26
(0.02) NS NS Values in each molecular subtype column are the mean
.+-. SD of the log2 normalized signal intensity for the best
representative cDNA probe for that gene. NS, P > 0.05. * CD109
does not have any representative probes on this microarray
platform. In the multivariate logistic regression analysis,
dependent variable is basal-like.
TABLE-US-00005 TABLE 5 Statistical analysis of biomarker in
molecular subgroups classified by IGS in the van de Vijver et al.
breast cancer microarray dataset (16). Univariate Wilcoxon
Multivariate Luminal HER2 Basal-like Rank Sum Test Logistic Mean
.+-. SD Mean .+-. SD Mean .+-. SD (Basal-like vs. Regression Gene
(Median) (Median) (Median) Other) P-value P-value FOXC1 -0.51 .+-.
0.21 (-0.50) -0.41 .+-. 0.21 (-0.41) 0.49 .+-. 0.42 (0.58)
<.0001 0.0028 CRYAB -0.36 .+-. 0.24 (-0.36) -0.29 .+-. 0.19
(-0.29) 0.27 .+-. 0.47 (0.28) <.0001 NS KRT5 -0.56 .+-. 0.40
(-0.50) -0.45 .+-. 0.42 (-0.28) 0.16 .+-. 0.56 (0.10) <.0001
0.0084 KIT -0.17 .+-. 0.24 (-0.16) -0.22 .+-. 0.25 (-0.19) 0.05
.+-. 0.34 (0.05) <.0001 NS CDH3 -0.49 .+-. 0.29 (-0.49) -0.14
.+-. 0.38 (-0.15) 0.32 .+-. 0.31 (0.37) <.0001 NS MSN -0.14 .+-.
0.17 (-0.13) -0.05 .+-. 0.16 (-0.06) 0.21 .+-. 0.14 (0.24)
<.0001 NS KRT17 -0.33 .+-. 0.28 (-0.35) -0.22 .+-. 0.39 (-0.14)
0.21 .+-. 0.46 (0.17) <.0001 NS EGFR -0.05 .+-. 0.14 (-0.05)
-0.01 .+-. 0.15 (-0.03) 0.07 .+-. 0.21 (0.06) <.0001 NS KRT14
-0.10 .+-. 0.12 (-0.11) -0.08 .+-. 0.13 (-0.11) 0.08 .+-. 0.30
(0.02) 0.0001 NS ITGB4 -0.03 .+-. 0.12 (-0.03) 0.10 .+-. 0.14
(0.08) 0.12 .+-. 0.19 (0.12) <.0001 NS Values in each molecular
subtype column are the mean .+-. SD of the log2 normalized signal
intensity for the best representative cDNA probe for that gene. NS,
P > 0.05. * FOXC2 and CD109 do not have any representative
probes on this microarray platform. In the multivariate logistic
regression analysis, dependent variable is basal-like.
[0097] For simplicity of data interpretation, normal breast-like
group was not included in the analysis. The normal breast-like
group resembles normal breast tissue samples with relatively high
expression of genes characteristic of adipose cells and other
non-epithelial cell types and low expression of luminal epithelial
cell genes. Because the normal-like classification was developed by
training on normal breast tissue, it has been speculated that the
normal-like subgroup may be mainly an artifact of having a high
percentage of normal "contamination" in tumor specimens (Parker et
al. 2009). Other explanations include a group of slow-growing
basal-like tumors that lack the expression of proliferation genes
or a potential new subtype called claudin-low tumors (Herschkowitz
et al. 2007). In addition, only some of the datasets used in our
analysis contain normal-like samples. FOXC1 was not found to be
overexpressed in these samples (data not shown).
[0098] Gene Signature Analysis. With the intent of developing a
gene signature associated with FOXC1 gene expression capable of
accurately detecting the basal-like subtype independent of IGS, the
test dataset that included normal breast tissue samples was first
analyzed (2). Genes that shared coordinate upregulation and genes
that shared coordinate downregulation with FOXC1 upregulation were
both included. Supervised stringent inclusion criteria were used
based on degree of Pearson correlation coefficients
(1.0>r>0.5 for genes with coordinate upregulation and
-1.0<r<-0.5 for genes with coordinate downregulation,
respectively). Only those genes that maintained their high degree
of correlation with FOXC1, independent of their individual
correlations with breast cancer subtypes, were included in the
final panel and validated in a total of 5 individually analysed
microarray datasets (Richardson et al. 2006; Farmer et al. 2005;
Ivshina et al. 2006; Miller et al. 2005-2, 13-15) and the ExpO
Project Database of the International Genomics Consortium (IGC) at
https://expo.intgen.org). The 30 genes that met the inclusion
criteria while still allowing for maximal applicability across
earlier generation microarray platforms (i.e. ranking in the top 30
genes associated with FOXC1 expression in 3 or more of the 5
datasets) are collectively referred to as the FOXC1 gene signature
(Table 6).
TABLE-US-00006 TABLE 6 Pearson correlation coefficients of the 30
genes associated with FOXC1 geneexpression in five microarray
datasets (2, 13-15). Dataset Gene Richardson Farmer Ivshina Miller
No. Symbol et al. ExpO et al. et al. et al. Frequency* 1 FOXC1 1.00
1.00 1.00 1.00 1.00 5 2 OGFRL1 0.86 0.50 0.49 0.65 0.66 4 3 ROPN1B
0.83 0.75 0.80 0.73 0.73 5 4 ART3 0.83 0.65 0.59 0.63 0.64 5 5
FABP7 0.82 0.39 0.40 0.57 0.60 3 6 C10orf38 0.82 0.65 0.70 0.72
0.72 5 7 EN1 0.81 0.74 0.80 0.74 0.73 5 8 KCNK5 0.80 0.63 0.64 0.65
0.64 5 9 CHODL 0.80 0.60 0.54 0.56 0.57 5 10 PRKX 0.80 0.55 0.73
0.66 0.66 5 11 C21orf91 0.79 0.56 0.39 0.52 0.53 4 12 GABRP 0.78
0.70 0.77 0.74 0.74 5 13 ELF5 0.77 0.65 0.63 0.61 0.61 5 14 PAPSS1
0.77 0.48 0.47 0.54 0.54 3 15 ACTR3B 0.77 0.64 0.63 0.55 0.55 5 16
LMO4 0.76 0.41 0.59 0.65 0.64 4 17 ZIC1 0.75 0.53 0.61 0.39 0.39 3
18 UGT8 0.75 0.64 0.46 0.60 0.60 4 19 MICALL1 0.75 0.70 0.78 0.64
0.64 5 20 FOXA1 -0.87 -0.75 -0.81 -0.82 -0.82 5 21 MLPH -0.86 -0.70
-0.78 -0.69 -0.69 5 22 SIDT1 -0.84 -0.58 -0.73 -0.56 -0.55 5 23
AGR2 -0.83 -0.67 -0.71 -0.59 -0.59 5 24 SPDEF -0.81 -0.64 -0.72
-0.79 -0.78 5 25 TFF3 -0.80 -0.53 -0.67 -0.46 -0.45 3 26 AR -0.80
-0.50 -0.56 -0.58 -0.59 5 27 TBC1D9 -0.79 -0.62 -0.66 -0.66 -0.66 5
28 CA12 -0.78 -0.60 -0.66 -0.66 -0.66 5 29 GATA3 -0.77 -0.56 -0.71
-0.70 -0.70 5 30 GALNT6 -0.75 -0.53 -0.66 -0.52 -0.51 5 *Frequency
denotes the number of datasets in which the correlation of
individual genes with FOXC1 expression is present (>0.50 for
coordinately upregulated genes, and <-0.50 for coordinately
downregulated genes, respectively).
[0099] To validate the ability of this gene signature to identify
basal-like breast cancer, in addition to the aforementioned 5
datasets used to refine the gene signature, another 6 publicly
available human breast cancer Affymetrix and cDNA microarray
datasets were individually tested (Hess et al. 2006; Herschkowitz
et al. 2007; van de Vijver et al. 2002; Sorlie et al. 2003; Wang et
al. 2005; Pawitan et al. 2005) representing analysis in a total of
2,073 breast cancer patients. All datasets were subjected to a
hierarchical clustering algorithm employing a Pearson uncentered
similarity metric and the average linkage rule based on the
30-member FOXC1 gene signature. Extent of correct classification of
breast cancer samples as belonging to the basal-like subtype was
compared to those classified based on IGS.
[0100] Survival Analysis. Next, the potential prognostic importance
of FOXC1 mRNA expression in breast cancer was determined, with
particular reference to assessing its ability to correctly predict
the survival of patients with basal-like breast cancer. This
analysis was performed with the intent to determine whether FOXC1
mRNA expression could be used as a stand alone, individual
prognostic biomarker for basal-like breast cancer instead of
pathologic, immunohistochemical and/or molecular classifiers such
as IGS. A 295-sample breast cancer oligonucleotide microarray
dataset (van de Vijver et al. 2002) with follow-up data extending
over a 20 year period was subjected to analysis. The prognostic
significance of FOXC1 was also examined in three additional human
breast cancer cDNA datasets: A 232-sample dataset (Herschkowitz et
al. 2007), a 122-sample dataset (Sorlie et. al. 2003), and a
159-sample dataset (Pawitan et al. 2005). Survival distributions
were estimated using Kaplan-Meier methods and compared using the
log-rank test. In multivariate survival analyses, Cox proportional
hazard regression model was used incorporating phenotype status
(basal-like versus non-basal-like), FOXC1 level, age, tumor size,
tumor grade, and lymph node status as possible predictors of
survival. Proportional hazard assumption was validated using
residual plots and proportionality tests. The relative prognostic
significance of two separate prognostic models was evaluated by
comparing the model fit after adjusting for clinicopathologic
variables. One model was based on dichotomous expression of FOXC1
mRNA levels. The other model was based on the IGS-derived
basal-like cluster following hierarchical clustering. The relative
prognostic significance of each model was measured using Akaike's
Information Criterion (AIC) to assess the fit of the two regression
models (Akaike 1974).
[0101] Association with metastasis to the brain or bone was
examined in lymph node-negative breast cancer patients in the Wang
et al. data set (Wang et al. 2005). 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 initial univariate
analysis and included age, tumor size, and lymph node status.
Survival plots were created using Kaplan-Meier methods.
[0102] Immunohistochemistry and Immunoblotting Immunohistochemistry
was performed using a peroxidase detection system with human breast
cancer tissue microarrays BRC961 and BR962 (US Biomax) and a
polyclonal FOXC1 antibody that does not recognize FOXC2 (Lifespan
Biosciences). Antibody concentration (1:100) was determined by
serial titration and optimisation of the antibody on test arrays.
Briefly, after antigen retrieval, primary antibodies were added,
followed by a biotinylated secondary antibody incubation, which
then binds to peroxidase-conjugated streptavidin. The signal was
developed with diaminobenzidine as the chromogen with hematoxylin
as counterstain. The immunostained slides were evaluated
microscopically by estimating the proportion and average intensity
of positive tumor cells with nuclear and/or cytoplasmic staining.
Immunohistochemical analysis was also performed on 42
triple-negative human breast cancer specimens obtained from the
Saint John's Health Center Department of Pathology and John Wayne
Cancer Institute tissue bank in accordance with Institutional
Review Board approval. Immunoblotting was performed using an
antibody from Santa Cruz Biotechnology. Whole cell lysates for
western blotting were generated by cell lysis buffer (50 mM
Tris-HCl, pH 7.4, 150 mM NaCl, 2 mM EDTA, 1% NP-40, 10% glycerol)
supplemented with a protease inhibitor cocktail (Sigma, St Louis,
Mo.). Equal amounts of protein were separated by 10% SDS-PAGE and
then transferred onto a nitrocellulose membrane. The remaining
steps were conducted according to a standard immunoblotting
protocol.
[0103] Results and Discussion
[0104] Gene expression analysis of publicly available human breast
cancer microarray data sets revealed that the Forkhead-box
transcription factor FOXC1, essential for mesoderm tissue
development, had significantly higher expression in the basal-like
subgroup than in other subtypes (FIGS. 1A, 1B, 2 and 3A-C). High
FOXC1 expression correlated positively and significantly with the
basal-like subgroup, as shown in Tables 2-5 above. Elevated FOXC1
mRNA expression was also associated with triple-negative breast
cancer, consistent with the notion that 60% to 90% of
triple-negative breast cancers are basal-like (FIGS. 1C and 3D). A
30-gene FOXC1 signature was derived from correlation with FOXC1
expression in six data sets (Table 6, above) and validated in five
separate data sets. These genes displayed an overall expression
profile that coincided with the basal-like subgroup clustered by
IGS (FIGS. 1D and 4). Conversely, hierarchical clustering using the
FOXC1 gene signature identified the same basal-like subgroup
determined by IGS (FIG. 5). Whereas pathway analysis of this gene
signature did not yield a dominant pathway (data not shown), some
members such as FABP7, GABRP, EN1, KCNK5, ZIC1, ACTR3B, and FOXC1
are notably involved in brain development and brain tumorigenesis,
which explains why BLBC preferentially metastasizes to the
brain.
[0105] FOXC1 protein expression was then evaluated using
immunohistochemistry 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.
6A) 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 (FOXC1+) was associated significantly with
expression of basal cytokeratins (FIG. 6B) and displayed a
sensitivity of 0.81 and a specificity of 0.80 in detecting the
basal-like phenotype identified by positive staining of CK5/6
and/or CK14. Absence of CK staining in some FOXC1+/ER-/PR-/HER2-
samples in this cohort may reflect inconsistent expression of these
cytokeratins in BLBCs defined by expression arrays (Nielsen et al.
2004). The finding that nuclear FOXC1 was consistently detected by
immunohistochemistry despite its short protein half-life (<30
minutes; Berry et al. 2006) 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 (FIG. 7), which was confirmed by
immunoblotting (FIG. 6C).
[0106] The prognostic significance of FOXC1 in breast cancer was
next examined in the 295-sample van de Vijver et al. data set (van
de Vijver et al. 2002). In univariate analysis, overall survival
was significantly worse in tumors identified using the 30-gene
FOXC1 signature (P=0.0004) or using elevated FOXC1 mRNA levels
alone (P=0.0001; FIG. 8A). Overall survival decreased by 35% for
each unit increase of relative FOXC1 mRNA levels. In multivariate
analysis, FOXC1 was an independent prognostic indicator of overall
survival after adjusting for clinicopathologic 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; Akaike 1974) 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. (Herschkowitz
et al. 2007), 122-sample Sorlie et al. (Sorlie et al. 2003), and
159-sample Pawitan et al. (Pawitan et al. 2005) data sets (FIG. 9).
Furthermore, the FOXC1 gene signature and mRNA levels, like the
basal-like phenotype, allowed prognostic stratification of lymph
node-negative breast cancers (P=0.0003) in the van de Vijver et al.
data set (an de Vijver et al. 2002; FIG. 8B). 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 (Wang et al.
2005), significantly correlated with shorter brain metastasis-free
survival (P<0.0001; FIGS. 8C and D).
Example 2
Quantitative Measurement of FOXC1 Expression Using RT-PCR can be
Used to Accurately Diagnose Basal-like Breast Cancer
[0107] Gene expression analysis has classified breast cancer into
five molecular subtypes. Basal-like breast cancer comprises up to
15%-25% of all breast cancers and is associated with the worst
overall survival. As described in the example above, FOXC1 is a
theranostic biomarker specific for basal-like breast cancer.
Semi-quantitative measurement of FOXC1 expression (microarray and
immunohistochemistry) has been shown as a reliable method to
diagnose basal-like breast cancer. These findings may be extended
and further refined by assessing FOXC1 expression using qRT-PCR to
provide a more quantitatively accurate assay for diagnosing
basal-like breast cancer.
[0108] Quantitative RT-PCR gene expression data from 279
formalin-fixed paraffin embedded (FFPE) breast tumors were obtained
from a publicly available database (J Clin Oncol. 2009 Mar. 10;
27(8):1160). The receiver operating curve-area under the curve
(ROC-AUC) was determined for FOXC1. A cut-off level was determined
to optimize sensitivity and specificity.
[0109] The ROC-AUC for FOXC1 expression (FIG. 10) in predicting
basal-like breast cancer was 0.807. A 74% sensitivity and 78%
specificity for identifying basal-like breast cancer was shown when
using the 0.437 (49.sup.th percentile) cut-off level for FOXC1
expression using qRT-PCR.
[0110] Quantitative RT-PCR assessment of FOXC1 is thus proven to be
a reliable assay to accurately diagnose basal-like breast cancer.
Quantitative RT-PCR assessment of FOXC1 from FFPE breast tumors is
proposed to be a useful adjunct to semi-quantitative assays
(microarray and immunohistochemistry) for the diagnosis of
basal-like breast cancer in routine clinical practice.
Example 3
Prognostic Significance of FOXC1 in Breast Cancer Molecular Subtype
Models Utilizing Immunohistochemical Biomarkers
[0111] In the studies described herein, the Forkhead-box
transcription factor FOXC1, essential for mesoderm tissue
development, was been shown to be consistently overexpressed at
both the mRNA and protein levels in BLBC. Elevated FOXC1 mRNA
expression was associated with poor overall survival, independent
of other clinicopathologic prognostic variables, including lymph
node status. True to a predilection for brain metastasis displayed
by patients with BLBC, high FOXC1 mRNA levels were also found to
correlate with the incidence of brain metastasis and with
significantly shortened brain-metastasis free survival in lymph
node negative patients. Furthermore, engineered, ectopic
overexpression of FOXC1 in breast cancer cells induced aggressive
phenotypic changes such as increased cellular proliferation,
migration and invasion. Knockdown of FOXC1 using shRNA in breast
cancer cells with high endogenous levels of FOXC1 demonstrated loss
of aggressive phenotypic features. These results suggest that FOXC1
is a specific prognostic biomarker for BLBC and plays an important
role in regulating aggressive cellular traits associated with this
molecular subtype. It may also serve as a suitable target for
personalized therapy of patients diagnosed with BLBC. These
findings utilizing gene expression profiling strongly support the
prognostic significance of FOXC1 mRNA expression in breast cancer.
According to the study described below, this finding is translated
or corroborated using assays of protein expression, such as
immunohistochemistry (IHC). Such an assay would be practical and
relevant for implementation into routine clinical practice.
[0112] Currently, breast cancer receptor status (ER, PR and HER2)
is widely used to perform prognostic stratification. Recent reports
have suggested using additional surrogate IHC markers of BLBC in
combination to improve prognostic stratification (Rakha et al.
2009; Nielsen et al. 2004; Cheang et al. 2008; Elsheikh et al.
2008). Therefore, three biomarker-based models of prognostic
stratification in breast cancer were compared: 1) the classic
3-biomarker panel comprising of ER, PR and HER2, 2) a 5-biomarker
panel comprising of the above receptors in combination with
traditional basal-like biomarkers, basal CK5/6 and CK14, and 3) a
4-biomarker panel comprising of ER, PR, and HER2, in combination
with FOXC1.
[0113] The primary objective of this study was to establish whether
the FOXC1 IHC assay has prognostic value in breast cancer. The
secondary objective was to compare the prognostic value of
molecular subtype models using surrogate IHC biomarkers in breast
cancer.
[0114] Methods
[0115] Patients. Review of a prospectively acquired institutional
database identified 904 patients with primary infiltrating ductal
breast cancer diagnosed between Jan. 1, 1995 and Dec. 31, 2004.
Patients who were diagnosed with stage 1V breast cancer at initial
presentation and who did not undergo primary surgical therapy at
John Wayne Cancer Center institution were excluded from the
analysis.
[0116] Translational study design. This translational study was
performed with institutional review board approval and is reported
according to the Reporting Recommendations for Tumor Marker
Prognostic Studies (REMARK) (McShane et al. 2005). Laboratory
personnel, who remained blinded to patient clinical data and
outcomes, performed all IHC assays. Assay results were interpreted
and scored by a single pathologist (JMS) who remained blinded to
the clinical and pathologic data. The design and statistical plan
were finalized before merging the above generated assay results
with the clinical data, prior to performance of data analysis.
[0117] Immunohistochemistry protocols. A board-certified
pathologist fellowship trained in breast pathology (JMS), who
remained blinded to the clinical and pathologic data reviewed IHC
(ER, PR, HER2) slides selected randomly from each pre-designated
group of patients based on receptor status. Approximately 20% of
the study cohort had such verification of receptor status
performed. This was done as an internal quality control measure to
ensure that the ER, PR and HER2 status of patients at the time of
performance of this study was in agreement with that initially
rendered at the time of initial diagnosis. No significant
difference was encountered in the course of this quality control
exercise. Biomarker expression status based on IHC assays was
scored using criteria from published guidelines. ER and PR status
were considered positive if immunostaining was seen in >10% of
tumor nuclei. HER2 status was considered positive if immunostaining
was scored as 3+ according to HercepTest criteria. For an equivocal
result (2+), HER2 status was considered positive if the fluorescent
in situ hybridization (FISH) assay revealed a HER2: chromosome 17
amplification ratio .gtoreq.2.2 (Yaziji et al. 2004).
[0118] Archival formalin-fixed paraffin embedded (FFPE) tissue
blocks for patients designated to be triple-negative with respect
to hormone receptor status, i.e., for those who were
ER.sup.-/PR.sup.-/HER2.sup.- were then obtained. Tissue blocks were
sectioned into serial 5 .mu.m thick tissue sections and subjected
to IHC analysis for CK5/6 (D5 and 16B4, Cell Marque Corp, Rocklin,
Calif.; no dilution), CK 14 (VP-C410, Vector Laboratories,
Burlingame, Calif.; dilution 1:20) and FOXC1 (Ray et al. 2010).
Semiquantitative analysis was performed by one pathologist (JMS)
blinded to clinical and pathological data who scored the intensity
of immunoreactivity on a scale of 0 (no staining) to 3 (strong
staining). CK5/6 and CK14 stains were considered positive if any
cytoplasmic and/or membranous invasive carcinoma cellular staining
was observed (Nielsen et al 2004). FOXC1 protein expression status
was considered positive only if any nuclear staining of tumor cells
was observed (Ray et al. 2010).
[0119] Immunohistochemcial definition of breast cancer molecular
subtypes. For purposes of this study, breast cancer molecular
subtypes were defined utilizing surrogate IHC biomarker panels as
has been earlier reported (Nielsen et al. 2004). ER and HER2 status
were used to define luminal (ER.sup.+/HER2.sup.-),
luminal/HER2.sup.+ (ER.sup.+/HER2.sup.+), HER2.sup.+
(ER.sup.-/HER2.sup.+) and basal-like (ER.sup.-/HER2.sup.-)
molecular subtypes. In addition to assessing the prognostic
significance of FOXC1 protein expression in breast cancer, the
prognostic significance of three separate surrogate IHC biomarker
panels was also compared and used to define BLBC: 1) the triple
negative phenotype or TNP, defining BLBC as being negative for the
routinely tested receptor biomarkers ER, PR and HER2, 2) a
5-biomarker panel comprising of TNP combined with CK5/6 and CK14,
defining BLBC as being negative for ER, PR and HER2 and positive
for either CK5/6 and/or CK14 expression, and 3) a 4-biomarker panel
comprising of TNP and FOXC1, defining BLBC as being negative for
ER, PR and HER2 and positive for FOXC1 protein expression. In the
5-biomarker and 4-biomarker models, the subset of TNP patients
negative for all biomarkers are referred to as 5NP and 4NP,
respectively.
[0120] Statistical Analysis. All statistical analyses were
performed using SAS (version 9.1.3, SAS, Cary, N.C.). Criteria used
to determine positive or negative status of a specific biomarker
were determined prior to performing any statistical analysis.
Analysis of categorical variables was performed using .chi..sup.2
test and Fisher's exact test. The Mann-Whitney U test was employed
to compare non-normal continous variables. For survival analysis,
overall survival (OS) was the outcome measure used. Survival time
was calculated as the date of diagnosis until the date of death.
Survival times were censored if the patient was still alive on Oct.
15, 2009 (the last date of update of the database). Univariate
survival curves were generated by the Kaplan-Meier method (Bland et
al. 1998) and significance determined using the log-rank test
(Bland J M, Altman D G. The logrank test. BMJ 2004; 328:1073).
Multivariate analysis was performed using Cox's proportional
hazards analysis. For purposes of evaluating the prognostic
significance of each of the above IHC biomarker panel definitions
of BLBC, three separate models were constructed for the
3-biomarker, 5-biomarker and 4-biomarker definitions of BLBC. The
three different multivariate models were compared using the
likelihood ratio test and Akaike's Information Criterion (AIC)
(Akaike 1974). In addition, we all hypotheses were tested using the
Wald test (Cox 1974) and associated P value. All tests were
two-sided and P values <0.05 were considered statistically
significant.
[0121] Results and Discussion
[0122] In this series of 904 patients diagnosed with primary
invasive ductal adenocarcinoma of the breast (FIG. 15), all
patients had pre-existing data with regard to IHC detection of ER,
PR and HER2 receptor status. Patients who were diagnosed with stage
1V breast cancer at initial presentation (n=19), who did not
undergo primary surgical therapy at John Wayne Cancer Institute
(n=125), were excluded from the analysis. The final sample size of
the study cohort was 759.
[0123] Clinicopathologic features of study cohort.
Clinicopathologic features of the 759 patients included in this
study appear in Table 7 (below) classified according to ER and HER2
status, approximating the molecular subtypes.
TABLE-US-00007 TABLE 7 Clinical and histopathologic characteristics
of the patient cohort - T stage and nodal status are based on final
pathologic assessment. Luminal Luminal/HER2 HER2 Basal-like
(ER.sup.+/HER2.sup.-) (ER.sup.+/HER2.sup.+) (ER.sup.-/HER2.sup.+)
(ER.sup.-/HER2.sup.-) n = 481 n = 95 n = 57 n = 126 Subtype (63.3%)
(12.5%) (7.5%) (16.7%) Age (mean .+-. SD) 58.3 .+-. 13.5 52.0 .+-.
11.7 53.5 .+-. 10.4 56.1 .+-. 15.2 Tumor size 0-2 cm 356 (74.0) 56
(59.0) 31 (54.4) 68 (54.0) 2-5 cm 102 (21.2) 27 (28.4) 17 (29.8) 40
(31.7) >5 cm 13 (2.7) 10 (10.5) 4 (7.0) 15 (11.9) Unknown 10
(2.1) 2 (2.1) 5 (8.8) 3 (2.4) Nodal status Negative 322 (66.9) 56
(58.9) 30 (52.6) 70 (55.6) Positive 140 (29.1) 38 (40.0) 26 (45.6)
48 (38.1) Unknown 19 (4.0) 1 (1.1) 1 (1.8) 8 (6.3) Tumor grade 1
149 (31.0) 1 (1.0) 0 (0) 1 (0.8) 2 225 (46.8) 30 (31.6) 8 (14.0) 11
(8.7) 3 101 (21.0) 62 (65.3) 47 (82.5) 109 (86.5) Unknown 6 (1.2) 2
(2.1) 2 (3.5) 5 (4.0) Hormonal therapy No 96 (20.0) 16 (16.8) 48
(84.2) 85 (67.5) Yes 328 (68.2) 67 (70.5) 3 (5.3) 9 (7.1) Unknown
57 (11.9) 12 (12.5) 6 (10.5) 32 (25.4) Chemotherapy No 238 (49.5)
21 (22.1) 8 (14.0) 24 (19.1) Yes 174 (36.2) 69 (72.6) 43 (75.5) 67
(53.2) Unknown 69 (14.4) 5 (5.3) 6 (10.5) 35 (27.8) Herceptin
therapy No -- 65 (68.4) 34 (59.7) -- Yes -- 21 (22.1) 14 (24.6) --
Unknown -- 9 (9.5) 9 (15.8) --
[0124] As illustrated in Table 7, 63.3% (481 of 759) were defined
as having Luminal (ER.sup.+/HER2.sup.-) subtype, 12.5% (95 of 759)
as having Luminal/HER2 (ER.sup.+/HER2.sup.+) subtype, 7.5% (57 of
759) as having HER2 (ER.sup.-/HER2.sup.+) subtype and 16.7% (126 of
759) were defined as being BLBC by the TNP definition (3-biomarker
panel). 90 of these 126 specimens underwent additional IHC assays
performed for assessment of CK5/6, CK14 and FOXC1. Analyses were
not performed for the 36 remaining specimens because of exhaustion
of invasive tumor tissue, inadequate remaining invasive tumor in
the tissue block or technical issues. 60 of 90 TNP patients were
BLBC by the basal cytokeratin definition (5-biomarker panel), and
55 of 87 TNP patients were basal-like by the FOXC1 definition
(4-biomarker panel). Clinicopathologic features of the TNP patients
classified according to either the 5-biomarker panel or the
4-biomarker panel appear in Table 8 below. Representative IHC
images of FFPE sections stained with CK5/6, CK14 or FOXC1 are shown
in FIG. 26.
TABLE-US-00008 TABLE 8 Clinicopathologic characteristics of patient
subset with triple negative breast cancer. Basal CK.sup.- Basal
CK.sup.+ FOXC1.sup.- FOXC1.sup.+ n = 38 n = 60 n = 42 n = 49 (5.6%)
(8.9%) p-value (6.3%) (8.2%) p-value Age (mean .+-. SD) 59.7 .+-.
14.4 55.9 .+-. 16.6 0.2429 63.2 .+-. 15.2 51.5 .+-. 14.4 0.0003
Tumor size 0-2 cm 24 (63.2) 25 (41.7) 19 (45.2) 23 (46.9) 2-5 cm 7
(18.4) 26 (43.3) 14 (33.3) 19 (38.8) >5 cm 7 (18.4) 7 (11.7) 8
(19.1) 6 (12.3) Unknown 2 (3.3) 1 (2.4) 1 (2.0) Nodal status
Negative 21 (55.3) 32 (53.4) 23 (54.7) 26 (53.1) Positive 15 (39.5)
23 (38.3) 13 (31.0) 22 (44.9) Unknown 2 (5.3) 5 (8.3) 6 (14.3) 1
(2.0) Tumor grade 1 1 (2.6) 0 (0) 0 (0) 0 (0) 2 4 (10.5) 4 (6.7) 6
(14.3) 2 (4.1) 3 32 (84.2) 54 (90.0) 35 (83.3) 45 (91.8) Unknown 1
(2.6) 2 (3.3) 1 (2.4) 2 (4.1) Hormonal therapy No 23 (60.5) 39
(65.0) 25 (59.6) 32 (65.3) Yes 4 (10.5) 1 (1.7) 3 (7.1) 1 (2.0)
Unknown 11 (29.0) 23 (33.3) 14 (33.3) 16 (32.7) Chemotherapy No 9
(23.7) 9 (15.0) 9 (21.4) 5 (10.2) Yes 17 (44.7) 30 (50.0) 17 (40.5)
28 (57.1) Unknown 12 (31.6) 21 (35.0) 16 (38.1) 16 (32.7) **p
value
[0125] Prognostic value of FOXC1 protein expression in breast
cancer. In the present study, FOXC1 status was considered positive
only if any nuclear staining was observed (Ray et al. 2010).
Positive expression of FOXC1 protein was found to be a significant
predictor of overall survival (FIG. 16) amongst breast cancer
patients on univariate analysis (HR 3.364 95% CI 1.758-6.438,
P=0.0002) (Table 9-10). Other standard clinicopathologic factors
such as age, tumor size, nodal status and tumor grade were also
found to be significant predictors of overall survival. Adjuvant
treatment variables such as hormonal therapy, chemotherapy or
trastuzumab (herceptin) therapy were not significant predictors of
overall survival, indicating equivalent effects across all groups.
Furthermore, the prognostic significance of FOXC1 on univariate
anlaysis was retained regardless of the cutoff point used to
segregate patients into FOXC1 positive and FOXC1 negative subsets
(Table 9, FIG. 16). The prognostic significance of FOXC1 protein
expression as an independent predictor of OS persisted on
multivariate analysis, whereas both the triple negative phenotype
as well as the basal cytokeratin positive phenotypes no longer
remained significant on multivariate analysis (Table 10). Again,
the prognostic significance of FOXC1 as an independent predictor of
OS on multivariate analysis was also retained regardless of the
cutoff point used to segregate patients into FOXC1 positive and
FOXC1 negative subsets. The optimal cutoff point for FOXC1 protein
expression scored on IHC in this study was 0-1 (n=42) versus 2-3
(n=49), although FOXC1 protein expression remained a highly
significant prognostic marker at all cutoff points tested (0 versus
1-3, 0-1 versus 2-3 and 0-2 versus 3).
TABLE-US-00009 TABLE 9 Univariate cox regression analysis of the
prognostic significance of individual clinicopathologic and
treatment variables on 5 year overall survival. N P-value Hazard
ratio (95% CI) Age 759 <0.0001 1.046 (1.028 1.064) Tumor Size
739 0.0006 1.826 (1.293 2.580) (>=5, 2-4.99, 0-2) Nodal Status
730 0.0113 1.913 (1.158 3.164) (Positive vs. Negative) Tumor Grade
(1, 2, 3) 744 0.0313 1.468 (1.035 2.082) ER.sup.-/HER2.sup.- vs.
others 759 0.0104 2.027 (1.181 3.480) Basal+ vs. others 731 0.0043
2.572 (1.344 4.919) FOXC1.sup.+ (1, 2, 3) vs. others 724 0.0014
2.880 (1.505 5.510) FOXC1.sup.+ (2, 3) vs. others 724 0.0002 3.364
(1.758 6.438) FOXC1.sup.+ (3) vs. others 724 0.0012 3.392 (1.618
7.112) Hormone Therapy (yes vs. no) 652 0.1213 0.660 (0.390 1.116)
Chemotherapy (yes vs. no) 644 0.2512 0.733 (0.432 1.245) Herceptin
Therapy (yes vs. no) 688 0.6389 1.275 (0.462 3.524)
TABLE-US-00010 TABLE 10 Multivariate cox regression analysis of the
prognostic significance of individual clinicopathologic and
treatment variables on 5 year overall survival. N P-value Hazard
ratio (95% CI) Age 670 <0.0001 1.049 (1.028 1.069) Tumor Size
0.0022 1.797 (1.234 2.618) (>=5, 2-4.99, 0-2) Nodal Status
(Positive vs. Negative) Tumor Grade (1, 2, 3) ER.sup.-/HER2.sup.-
vs. others Basal+ vs. others FOXC1.sup.+ (1, 2, 3) vs. others
*0.0005 3.406 (1.713 6.775) FOXC1.sup.+ (2, 3) vs. others *0.0001
3.839 (1.928 7.645) FOXC1.sup.+ (3) vs. others *0.0019 3.755 (1.632
8.636) Hormone Therapy (yes vs. no) Chemotherapy (yes vs. no)
Herceptin Therapy (yes vs. no)
[0126] Overall survival according to IHC models of breast cancer
molecular subtype. The breast cancer subtypes as defined by the
surrogate IHC biomarker panels differed significantly in predicting
OS (FIG. 17). The model utilizing FOXC1 achieved the most
significant degree of prognostic stratification (p<0.0001). In
the 3-biomarker panel, the 5-year and 10-year OS for BLBC patients
(defined using TNP) was 85% and 77%, respectively. In the
5-biomarker panel, the 5-year and 10-year OS for BLBC patients
(defined using TNP+CK5/6 and CK14.sub.-- was 82% and 66%,
respectively. In the 4-biomarker panel, the 5-year and 10-year OS
for BLBC patients (defined using TNP+FOXC1) was 77% and 69%,
respectively.
[0127] On univariate Cox regression analysis, in addition to
standard clinicopathologic factors such as age, tumor size, lymph
node status and tumor grade, BLBC defined according to the
3-biomarker, 5-biomarker and 4-biomarker panels were all
significant predictors of breast cancer OS (Table 9, above). On
multivariate Cox regression analysis, only age, tumor size and BLBC
defined according to the 4-biomarker panel based on FOXC1 protein
expression retained significance and were independent predictors of
OS. The 3-biomarker panel utilizing TNP as well as the 5-biomarker
panel based on basal CK expression lost significance on
multivariate analysis.
[0128] For purposes of evaluating the prognostic significance of
each of the above IHC biomarker panel definitions of BLBC, three
separate multivariate models of breast cancer molecular subtypes
were constructed for the 3-biomarker (based on the triple negative
phenotype (TNF)), 5-biomarker (based on expression of basal
cytokeratins) and 4-biomarker (based on protein expression of
FOXC1) definitions of BLBC, each including the standard
clinicopathologic factors age, tumor size, nodal status and tumor
grade. The three multivariate models were compared using the
likelihood ration test and Akaike's Information Criterion (AIC).
The 4-biomarker model based on FOXC1 protein expression had the
lowest AIC score indicating it to be the model with the greatest
prognostic value (Table 11).
TABLE-US-00011 TABLE 11 Comparison of the three different
multivariate models of breast cancer molecular subtype utilizing
surrogate immunohistochemical biomarker panels. 3-biomarker (TNP)
AIC = 748.576 prognostic model N = 702 P-value Hazard Ratio (95%
CI) Age <0.0001 1.049 1.029 1.069 Tumor Size (>5, 2-4.99,
0-2) 0.0153 1.600 1.094 2.338 Nodal Status (positive vs. negative)
Tumor Grade (High-3, 0.0123 1.628 1.111 2.385 Intermediate-2,
Low-1) ER.sup.-/HER2.sup.- vs. others 5-biomarker (Basal
cytokeratin) AIC = 719.774 prognostic model N = 677 P-value Hazard
Ratio (95% CI) Age <0.0001 1.042 1.022 1.063 Tumor Size (>5,
2-4.99, 0-2) 0.0034 1.765 (1207 2.581) Nodal Status (positive vs.
negative) Tumor Grade (High-3, Intermediate-2, Low-1) Basal.sup.+
vs. others 0.01 2.499 1.245 5.016 4-biomarker (FOXC1) AIC = 712.989
prognostic model N = 670 P-value Hazard Ratio (95% CI) Age
<0.0001 1.045 1.028 1.069 Tumor Size (>5, 2-4.99, 0-2) 0.0022
1.797 1.234 2.618 Nodal Status (positive vs. negative) Tumor Grade
(High-3, Intermediate-2, Low-1) FOXC1.sup.+ (2, 3) vs. others
<0.0001 3.839 1.928 7.645
[0129] In the current study cohort of patients with invasive ductal
breast cancer, the basal-like phenotype defined on the basis of
positive FOXC1 protein expression was superior to the traditionally
employed triple negative phenotype, for purposes of prognostic
stratification. This demostrates that being "basal-like" is not
synonymous with being "triple-negative." The IHC definition of the
basal-like phenotype based on the positive expression of FOXC1
protein was also superior to basal-like phenotype defined by the
positive expression of basal CK, for purposes of prognostic
stratification. This represents a significant advance as, unlike
basal CKs, FOXC1 represents a potential candidate for the targeted
personalized therapy of patients with BLBC (Ray et al. 2010). FOXC1
not only promises to be a prognostic biomarker, but a predictive
biomarker as well--predictive of the therapeutic efficacy of any
future anti-FOXC1 directed drug or biologic for the treatment of
patients with basal-like breast cancer.
[0130] The tissue microarray platform relies on representative core
needle sampling of specimens and is an excellent method for
exploratory research projects that considerably minimizes resource
allocation. It is ideal for assessing the presence of biomarkers
that are expressed homogeneously throughout a specimen such as ER
and HER2. However, it is not ideal for assessing the presence of
potential biomarkers, such as basal CKs, that are expressed
heterogeneously throughout the tissue section (refer Laakso et
al.). Therefore, entire tissue sections were used instead of tissue
microarrays for the analysis.
[0131] The analysis discussed above was restricted to the invasive
ductal breast cancer histologic type. This was done to minimize
potential confounding effects (prognostic, biologic or both) of
histologic subtype on molecular subtype in breast cancer. However,
the above findings with regard to FOXC1 protein expression may be
extrapolated to other histologic types of breast cancer such as
lobular breast cancer.
[0132] FOXC1 mRNA expression, is found to have a prognostic impact
on OS in breast cancer that is likely independent of lymph node
status, and is at least in part attributable to a significantly
higher rate of association with the early occurrence of brain
metastasis, often as the first site of distant metastasis, even in
lymph node negative patients. In the present study, when FOXC1
protein expression status as assessed by IHC was included in the
multivariate model, nodal status failed to retain significance.
This lends further support to the prognostic impact of FOXC1 being
independent of nodal involvement.
[0133] The 4-biomarker panel utilizing FOXC1 protein expression
showed superior prognostication compared to the 5-biomarker panel
utilizing basal CK 5/6 and/or CK14 in the current patient cohort
(when considered in combination with ER, PR and HER2 status of
breast cancer specimens). This suggests that FOXC1 protein
expression, when present, is successful in diagnosing patients
possessing the true basal-like molecular subtype from amongst
patients with the triple-negative phenotype. A subset analysis of
only triple-negative patients in this study cohort displayed a
trend towards supporting this conclusion (data not included).
Example 4
FOXC1 Responsible for Aggressive and Invasive Phenotype, Making it
a Viable Therapeutic Target
[0134] Materials and Methods
[0135] 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 .mu.g/mL puromycin. Pooled knockdown cells were
used for experiments.
[0136] FOXC1 shRNAs. The following shRNAs were purchased from
Sigma: Mouse FOXC1 shRNA sequences:
TABLE-US-00012 (shRNA1; SEQ ID NO: 1)
CCGGGAGCAGAGCTACTATCGCGCTCTCGAGAGCGCGATAGTAGCTCTG CTCTTTTG; and
(shRNA2; SEQ ID NO: 2)
CCGGTGGGAATAGTAGCTGTCAGATCTCGAGATCTGACAGCTACTATTC CCATTTTTG.
Human FOXC1 shRNA sequences:
TABLE-US-00013 (shRNA1; SEQ ID NO: 3)
CCGGCAAGAAGAAGGACGCGGTGAACTCGAGTTCACCGCGTCCTTCTTC TTGTTTTTG; and
(shRNA2; SEQ ID NO: 4)
CCGGCCCGGACAAGAAGATCACCCTCTCGAGAGGGTGATCTTCTTGTCC GGGTTTTT.
Control shRNA (does not target any known human or mouse gene):
TABLE-US-00014 (SEQ ID NO: 5)
CCGGCAACAAGATGAAGAGCACCAACTCGAGTTGGTGCTCTTCATCTTGT TGTTTTT
[0137] FOXC1-overexpressing cells. A full-length human FOXC1 cDNA
was stably transduced into breast cancer cells. Stable cell lines
were selected with 800 .mu.g/mL G418. Pooled populations were used
for experiments.
[0138] 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.
[0139] Cell migration and invasion assay. Briefly, 10.sup.4 cells
were plated on the top of a Boyden chamber inserts with an 8 .mu.m
pore size. The inserts were then transferred into a 24-well plate.
Each well contained DMEM with 10% serum as the chemoattractant. To
rule out the effect of cell proliferation, 2 .mu.g/ml mitomycin C
was added to the cells. After incubation, cells remaining on the
upper surface of the chambers were removed with cotton swabs. Cells
on the lower surface of the inserts were stained with the HEMA3 kit
(Fisher). The membrane was then mounted onto a microscope slide and
the migrating cells were counted in 5 different areas using a light
microscope. For invasion assays, inserts were coated with a thin
layer of Matrigel basement membrane matrix (BD Biosciences) and the
same procedures were followed.
[0140] Immunohistochemistry and Immunoblotting were performed as
described above.
[0141] Results and Discussion
[0142] The function of FOXC1 in breast cancer cells was examined.
Overexpression of FOXC1 in MDA-MB-231 BLBC cells (harboring
moderate levels of endogenous FOXC1) increased cell proliferation,
migration, and invasion (FIG. 11A). Similar results were observed
in MCF-7 luminal breast cancer cells (harboring undetectable levels
of endogenous FOXC1; FIG. 12A). FOXC1 overexpression also enhanced
anchorage-independent growth of MCF-7 cells in soft agar.
Immunoblotting indicated that cyclin D1, fibroblast markers
(vimentin, fibronectin, and .alpha.-smooth muscle actin), integrins
.beta.4 and .beta.1, and matrix metalloproteinases MMP2 and MMP9
were upregulated by FOXC1 overexpression (FIG. 12B-D). FOXC1 has
been shown to induce epithelial-mesenchymal transition (EMT) in
MCF-12A mammary epithelial cells (Bloushtain-Qimron et al.
2008-21). Similarly, FOXC1 overexpression in MCF-10A mammary
epithelial cells induced a mesenchymal phenotype accompanied by
increased expression of the basal marker P-cadherin and decreased
expression of the epithelial marker E-cadherin (FIG. 12E).
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 BLBC cells.
[0143] To assess the effects of FOXC1 depletion, FOXC1 shRNA was
stably transduced into 4T1 mouse breast cancer cells, which are a
model for stage 1V human breast cancer (Aslakson & Miller
1992-22) and possess high levels of endogenous FOXC1 (FIG. 13A).
These shRNAs reduced FOXC1 levels by >90% (FIG. 13B) and
decreased cell proliferation, migration, and invasion (FIG. 11B).
Similar results were obtained with BT549 human breast cancer cells
when FOXC1 was reduced by shRNA (FIGS. 13C 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 (FIGS. 11C and D). These data further
suggest a role of FOXC1 in regulation of cell function. Studies
have suggested that BLBC may possess extraordinarily high growth
rates (Seewaldt & Scott 2007) and an EMT phenotype (Sarrio et
al. 2008) compared with other breast cancer subgroups. FOXC1 may
play a role in coordinating these BLBC properties. Further, DNA
methylation may play a role in BLBC-associated FOXC1 expression. In
summary, these studies support FOXC1 as a theranostic biomarker,
i.e., a diagnostic and prognostic biomarker as well as a
therapeutic target.
Example 5
FOXC1 Regulation of ER.alpha. Expression and Function
[0144] Based on the studies below, it was found that FOXC1 induces
NF-.kappa.B signaling to inhibit ER.alpha. expression. This study
provides a molecular basis for the ER.alpha.-negative phenotype of
basal-like breast cancer and also provides implications for the
role of FOXC1 in the response of breast cancer cells to
antiestrogen treatment.
[0145] Materials and Methods
[0146] Cell Culture. MCF-7 and T47D human breast cancer cell lines
were obtained from the Breast Center at Baylor College of Medicine.
Cells were routinely maintained in Dulbecco's modified Eagle's
medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 2 mM
glutamine, 50 IU/ml of penicillin, 50 .mu.g/ml of streptomycin, and
10 .mu.g/ml insulin. Cells were kept at 37.degree. C. in a
humidified incubator with 5% CO.sub.2. Tamoxifen and
17.beta.-estradiol were from Sigma (St Louis, Mo.). The IKK small
molecule inhibitor BMS-345541 was purchased from Calbiochem
(Gibbstown, N.J.). For experiments involving estradiol and
tamoxifen, cells were serum-starved overnight and then stimulated
with the ER ligands for different time periods prior to cell
proliferation assays.
[0147] Microarray data analysis. Raw expression data from publicly
available human breast cancer gene expression microarray data sets
(Ginestier et al., 2006; Lu et al., 2008; Perou et al., 2000;
Pollack et al., 2002; Richardson et al., 2006; Schuetz et al.,
2006; Sorlie et al., 2001; Sorlie et al., 2003; Zhao et al., 2004)
and the Oncology--Breast Samples Project database (Bittnet et al.)
of the International Genomics Consortium (IGC) at
https://expo.intgen.org/expo/public were analyzed using Oncomine
4.0 software.
[0148] Stable transfection. MCF-7 and T47D cells were stably
transfected for 24 h with a FLAG-tagged FOXC1 construct or the
empty vector using Lipofectamine 2000 reagent (Invitrogen). Stable
clones were then selected using 800 .mu.g/ml G418 (Invitrogen).
Expression of FLAG-FOXC1 was verified by western blotting with an
anti-FOXC1 antibody (Santa Cruz Biotechnology, Santa Cruz, Calif.)
and an anti-FLAG antibody (Origene, Rockville, Md.).
[0149] Transient transfection. MCF-7 cells were grown for 48 h till
80% confluence before transfection. For cotransfections, 0.1 .mu.g
DNA of ERE-tk-luc or NF-.kappa.B-luc (Promega, Madison, Wis.)
reporter construct and 1 .mu.g of FLAG-FOXC1 or NF-.kappa.B p65
vector was added to 60 mm dishes. The transfected cells were
cultured for 24 h. The estrogen-responsive reporter plasmid
ERE-tk-luc contains a single consensus ERE upstream of a minimal
thymidine kinase promoter and the luciferase gene (Cui et al.,
2003). At 24 h after transfection, cells were washed twice with PBS
and harvested in 200 .mu.l of reporter lysis buffer (Promega).
Twenty nanograms of a .beta.-galactosidase expression vector
pSV-.beta.-Gal (Promega) were co-transfected as an internal
control. Luciferase and .beta.-galactosidase assays were performed
using Promega reporter assay reagents and the GloMax
Multi-detection system. To test whether p65 overexpression inhibits
ER.alpha. expression, MCF-7 cells were transfected with a p65
construct or the vector for 48 h, followed by immunoblotting.
[0150] Immunoblot analysis. Whole cell lysates for western blotting
were generated by cell lysis buffer (50 mM Tris-HCl, pH 7.4, 150 mM
NaCl, 2 mM EDTA, 1% NP-40, 10% glycerol) supplemented with a
protease inhibitor cocktail (Sigma, St Louis, Mo.). Equal amounts
of protein were separated by 10% SDS-PAGE and then transferred onto
a nitrocellulose membrane. The remaining steps were conducted
according to a standard immunoblotting protocol (Qu et al., 2009).
Immunoblotting was done with polyclonal antibodies against p65,
FOXC1, IRS1 (1:200; Santa Cruz Biotechnology), polyclonal
antibodies against phospho-p65, p50, I.kappa.B.alpha. (1:1000; Cell
Signal), or monoclonal antibodies against ER.alpha. (1:500;
Novocastra Laboratories, Newcastle upon Tyne, UK), PR (1:500; DAKO,
Carpinteria, Calif.). Anti-.beta. actin (Sigma) was used at a
1:10000 dilution. After the primary antibody incubation, the
membrane was again washed with PBST three times (5 min each) and
then incubated with a horseradish peroxidase (HRP)-linked secondary
antibody (Amersham, Piscataway, N.J.) at a dilution of 1:4000 in
blocking solution. The membrane was washed and bands were
visualized using chemiluminescence assays.
[0151] Real-time reverse transcription-PCR. Total RNA was isolated
from breast cancer cells using the RNeasy mini kit (Qiagen,
Valencia, Calif.). PCR amplification was performed by using
Rotor-Gene 3000 Real Time PCR System (CoRbett Research) in a
25-.mu.L reaction volume. The PCR mixture contained
SuperScript.RTM. III Reverse Transcriptase, TaqMan probe, and
forward and reverse primers. Samples were incubated for 1 cycle at
95.degree. C. for 2 min, 40 cycles at 95.degree. C. for 30 s, and
60.degree. C. for 60 s. All samples were run in triplicate. Results
were analyzed by using the Rotor-Gene 3000 software package
(Corbett Research). Primer information is as follows: FOXC1 forward
primer 5'-CGGTATCC AGCCAGTCTCTGTACCG-3' (SEQ ID NO:6), FOXC1
reverse primer 5'-GTTCGGCTTTGAGGGTGTGTC-3' (SEQ ID NO:7), ER.alpha.
forward primer 5'-CGGTTAGATTCATCATGCGGAACCG-3' (SEQ ID NO:8), and
ER.alpha. reverse primer 5'-TGTGTAGAGGGCATGGTGGAG-3' (SEQ ID NO:9).
ER.alpha. and FOXC1 mRNA data were normalized by the .beta.-actin
mRNA value.
[0152] Immunofluorescence staining. MCF-7 cells were transiently
transfected with GFP-FOXC1 plasmid for 24 h. Then the cells were
digested with trypsin and seeded in chamber slides (BD Biosciences,
Franklin Lakes, N.J.). After 12-h incubation, cells were fixed with
4% formaldehyde and then permeabilized with PBS containing 0.1%
Triton X-100. Slides were blocked by 5% BSA for 30 minutes and
incubated with a primary anti-ER.alpha. antibody (1:100) at room
temperature for 1 h. Cells were then incubated with an Alexa Fluor
546--conjugated secondary antibody (1:200, Invitrogen) for 30 min.
Slides were washed by PBS three times for 5 minutes each, mounted
in DAPI, and observed under a high resolution Nikon TI-E
microscope.
[0153] IPA signaling pathway analysis. The Richardson et al. data
set (Richardson et al., 2006) was subjected to Ingenuity Pathway
Analysis (IPA, Ingenuity Systems, Redwood City, Calif.). Briefly,
global gene expression profiles of all breast cancer samples were
analyzed according to their molecular subgroup (basal-like, HER2
and luminal) with respect to their association with a specific
canonical pathway in the Ingenuity Pathways Knowledge Base. The
significance of the association between the average global gene
expression profile associated with a particular subgroup and the
specific canonical pathway was measured in two ways: 1) A ratio of
the average number of genes from a particular subgroup that map to
the pathway divided by the total number of genes (having probe
representation on the microarray platform) assigned to the
canonical pathway was calculated. 2) Fischer's exact test was used
to calculate a p-value determining the probability that the
association between the genes in any particular subgroup and the
canonical pathway is explained by chance alone. The negative log of
this p-value is the Impact Factor.
[0154] NF-.kappa.B transcription factor TransAM assay. NF-.kappa.B
family activity was measured using the TransAM NF-.kappa.B ELISA
kit (Active Motif, Carlsbad, Calif.) according to the
manufacturer's instructions. Briefly, isolated nuclear pellets were
resuspended in extraction buffer (20 mM Hepes pH 7.9, 0.4 M NaCl, 1
mM EDTA). Supernatant (nuclear extract) was retained after a second
centrifugation. Samples (10 .mu.g) were added in triplicate to
96-well plates coated with an oligonucleotide that contains a
consensus binding sequence for NF-.kappa.B components. After 1 h
incubation at room temperature, primary antibodies of distinct
NF-.kappa.B components were added; subsequent addition of
HRP-conjugated secondary antibody produced a sensitive colorimetric
readout quantified by spectrophotometry at the 450-nm wavelength
with a reference wavelength of 655 nm.
[0155] Cell proliferation assay. Cell viability was assessed by the
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay.
Cells were seeded in 24-well plates at 30% confluence and the MTT
assay was performed one, two, three and four days after treatment.
For each assay, 50 .mu.l of MTT (5 mg/ml) were added to each well
and cells were incubated at 37.degree. C. for an additional 4 h.
After centrifugation, the supernatant was carefully aspirated and
300 .mu.l of DMSO (Sigma) were added to each well. Immediately
after resolubilization, all plates were scanned at 575 nm on a
microplate reader. The absorbance (A) value represented the number
of live cells.
[0156] Chromatin immunoprecipitation (ChIP) assay. ChIP assays were
performed by using a CHIP-IT Express Enzymatic kit (Active Motif)
according to the manufacturer's protocol. Cells were grown to 80%
confluence in DMEM supplemented with 10% FBS and then cross-linked
with 1% formaldehyde at room temperature for 10 min. Cells were
harvested and digested with trypsin, followed by centrifugation.
Supernatants were precleared at 4.degree. C. for 30 min with salmon
sperm DNA-protein A-Sepharose and immunoprecipitated with an
anti-p65 antibody (Santa Cruz Biotechnology) overnight at 4.degree.
C. Immunoprecipitation with normal rabbit IgG was performed to
evaluate the presence of non-specific interactions, and aliquots of
DNA-protein complexes were analyzed by PCR to normalize for DNA
input. Immunocomplexes were incubated with salmon sperm DNA-protein
A Sepharose for 1 h at 48.degree. C. Pellets were washed and eluted
as per the manufacturer's instructions and then incubated overnight
at 65.degree. C. DNA fragments were purified with a QIAquick Spin
Kit (Qiagen, Valencia, Calif.). The primers used for the ChIP
assays are as follows: ER.alpha. forward primer,
5'-AGAAGCTAGACCTCTGCAGG-3' (SEQ ID NO:10), and ER.alpha. reverse
primer, 5'-AAGCAG GGGCAAGGAAATATC-3' (SEQ ID NO:11). The amplified
140-bp fragment spans a conserved p65 binding site GGGACTTTCT in
the F promoter. For PCR, 2 .mu.l from a 30-.mu.l DNA extraction and
30 cycles of amplification were used.
[0157] Statistical Analysis. The results are presented as
mean.+-.standard deviation (SD) of samples measured in triplicate
or duplicate. Each experiment was repeated three times, unless
otherwise indicated. The Student's t-test was used to calculate
differences between the various experimental groups. The difference
was considered statistically significant with P<0.05.
[0158] Results and Discussion
[0159] FOXC1 is associated with ER.alpha.-negative human breast
cancer. FOXC1 has been identified as a pivotal marker for
basal-like breast cancer (Ray et al., 2010), which is characterized
by low or absent expression of ER, PR, and HER-2/neu. Analysis of
the Oncomine database, which provides publicly available gene
expression profiling datasets on human cancers, revealed that FOXC1
mRNA levels inversely correlated with ER.alpha. expression in
multiple breast cancer cDNA microarray array data sets (Ginestier
et al., 2006; Lu et al., 2008; Perou et al., 2000; Pollack et al.,
2002; Richardson et al., 2006; Schuetz et al., 2006; Sorlie et al.,
2001; Sorlie et al., 2003; Zhao et al., 2004) (FIGS. 18A and 19).
Next, FOXC1 levels in well-known ER.alpha.-positive or -negative
human breast cancer cell lines were examined. Immunoblotting
demonstrated that FOXC1 was readily detected in ER.alpha.-negative
breast cancer cell lines, but not in ER.alpha.-positive cells (FIG.
18B).
[0160] FOXC1 downregulates ER.alpha. expression. In light of the
strong inverse correlation between FOXC1 and ER levels in breast
cancer, it was determined whether FOXC1 affects ER.alpha.
expression. To address this question, FOXC1 was stably transfected
into ER.alpha.-positive MCF-7 breast cancer cells. Ectopic
overexpression of FOXC1 substantially reduced ER.alpha. levels in
stable transfectants, as shown by reverse transcription-PCR
(RT-PCR) and western blotting (FIGS. 20A and 20B). In accordance,
well-established estrogen-regulated genes PR and insulin receptor
substrate-1 (IRS-1) were also downregulated in FOXC1-overexpressing
MCF-7 cells (FIG. 20B). Similar results were also observed in
ER.alpha.-positive T47D breast cancer cells (FIG. 21).
[0161] To corroborate the above finding, a GFP-FOXC1 fusion gene
construct was transiently transfected into MCF-7 cells.
Immunofluorescence staining demonstrated that ER.alpha. levels were
markedly lower in MCF-7 cells expressing GFP-FOXC1 compared with
neighboring cells harboring barely detectable GFP signal (FIG.
20C). Next, MCF-7 cells with an estrogen response element
(ERE)-luciferase reporter construct were transiently co-transfected
as described previously (Cui et al., 2003), and a FOXC1 plasmid,
and then stimulated the cells with estradiol. As illustrated in
FIG. 20D, FOXC1 suppressed estradiol-induced luciferase activity,
suggesting that the transcriptional activity of ER.alpha. was
inhibited. Taken together, these results indicate that FOXC1 is a
repressor of ER.alpha. expression and thereby its activity.
[0162] FOXC1 reduces the sensitivity of breast cancer cells to
ER.alpha. ligands. Previously, FOXC1 overexpression was shown to
enhance cell growth under normal culture conditions (Ray et al.,
2010). Thus, it was determined whether FOXC1 affects the growth of
MCF-7 cells under other culture conditions. As illustrated in FIGS.
22A and 22B, FOXC1 overexpression potentiated the growth of MCF-7
cells in serum-free medium, but diminished the increase of cell
growth induced by estradiol treatment compared with serum-starved
conditions. In addition, FOXC1 overexpression rendered MCF-7 cells
less sensitive to the treatment of the antiestrogen tamoxifen (FIG.
22C). Collectively, these data suggest that the downregulation of
ER.alpha. by FOXC1 enables MCF-7 cell growth to be less dependent
on E2-induced ER.alpha. activation or tamoxifen-induced ER.alpha.
inactivation.
[0163] FOXC1 upregulates NF-.kappa.B activity. Because analysis of
the human ER.alpha. gene promoter (Kos et al., 2001; Tanimoto et
al., 1999) did not find conserved FOXC1-binding sites, it was
postulated that the inhibition of ER.alpha. by FOXC1 may be
mediated by other signaling mechanisms. With this in view, an
unbiased screening approach was adopted. As FOXC1 is an important
marker for basal-like breast cancer, a systematic signaling network
analysis of breast cancer cDNA microarray data sets was conducted
using the Ingenuity IPA platform (see Materials and Methods) to
identify basal-like breast cancer-associated signaling pathways. As
illustrated in FIG. 23A, NF-.kappa.B was uncovered as one of the
most distinctive pathways in the basal-like subtype, which is
consistent with the previous finding that the NF-.kappa.B
transcription factor is constitutively activated in ER-negative
breast cancer and essential for the proliferation of basal-like
breast cancer cells (Karin et al., 2002; Nakshatri et al., 1997;
Singh et al., 2007).
[0164] Given the above finding, it was determined whether FOXC1
regulates NF-.kappa.B function. Immunoblotting showed that the p65
subunit and p-p65 (Ser546, an I.kappa.B kinase [IKK]
phosphorylation site) were markedly induced by FOXC1 overexpression
in MCF-7 cells (FIG. 23B). Conversely, knockdown of FOXC1 by its
shRNA repressed p65 expression in 4T1 mouse breast cancer cells,
which possess high levels of endogenous FOXC1 (FIG. 23C).
Previously it was shown that p65 levels are primarily controlled at
the protein stability level (Ryo et al., 2003). Using RT-PCR and
the protein translation inhibitor cycloheximide, this p65
upregulation by FOXC1 was confirmed to be via an increase in its
protein stability (data not shown). Immunoblotting using nuclear
extracts indicated that FOXC1 promoted p65 translocation into the
nucleus (FIG. 23D). In agreement, TransAM ELISA using
oligonucleotides comprising consensus NF-.kappa.B-binding sequences
showed that FOXC1 considerably increased the DNA-binding activity
of p65 (FIG. 23E). To corroborate that FOXC1 enhances NF-.kappa.B
activity, an NF-.kappa.B-responsive luciferase reporter construct
was used. As illustrated in FIG. 23F, FOXC1 overexpression
significantly increased NF-.kappa.B-driven luciferase activity.
Co-expression of a super-repressor I.kappa.B.alpha., a
p65-inhibiting protein, abolished this FOXC1 effect. Interestingly,
FOXC1 overexpression sensitized MCF-7 cells to pharmacologic
inhibition of NF-.kappa.B by its small-molecule inhibitor
BMS-345541 in cell proliferation assays (FIG. 23G). Similar results
were obtained with other ER.alpha.-positive breast cancer cell
lines (data not shown). Taken together, these results demonstrate
that FOXC1 is a potent inducer of NF-.kappa.B activation.
[0165] NF-.kappa.B downregulates ER.alpha. expression. NF-.kappa.B
is associated with ER.alpha. negative status in breast cancer
(Biswas et al., 2004; Nakshatri et al., 1997). It has been shown
that NF-.kappa.B negatively regulates ER.alpha. expression (Biswas
et al., 2005; Holloway et al., 2004). To further investigate
whether NF-.kappa.B plays a role in the effect of FOXC1 on
ER.alpha. expression, the NF-.kappa.B p65 subunit in MCF-7 cells
was overexpressed by transfection. Immunoblotting showed that
increased p65 expression lowered ER.alpha. protein levels in MCF-7
cells (FIG. 24A). Real-time RT-PCR indicated that ER.alpha. mRNA
levels were also decreased (FIG. 25). Conversely, inhibition of
NF-.kappa.B by the IKK inhibitor BMS-345541 in FOXC1-overexpressing
MCF-7 cells elevated levels of ER.alpha., PR, and IRS-1 (FIG. 24B).
In addition, when p65 or ER.alpha. was transiently co-transfected
with a ERE luciferase reporter construct, E2-induced luciferase
activity was substantially decreased by p65 co-transfection, while
increased by ER.alpha. co-transfection (FIG. 24C).
[0166] The human ER.alpha. mRNA is transcribed from at least seven
different promoters with unique 5'-untranslated regions (Kos et
al., 2001). All these ER.alpha. transcripts utilize a same splice
accept site at nucleotide +163 from the transcription start site in
the originally identified exon 1 (Green et al., 1986). Previous
studies showed that p65 binds to the B promoter of the ER.alpha.
gene (Tanimoto et al., 1999). Notably, there is also a highly
conserved p65-binding site GGGACTTTCA at position -430 in the
ER.alpha. F promoter (Mahmoodzadeh et al., 2009). To confirm that
p65 binds to this promoter region, chromatin immunoprecipitation
(ChIP) assays were performed using cell extracts prepared from
control and FOXC1-overexpressing MCF-7 cells. The 140 bp amplified
promoter region spans the binding site. As presented in FIG. 24D,
p65 binding to the ER.alpha. F promoter was increased by FOXC1
overexpression. Taken together, these results suggest that p65
mediates the FOXC1 suppression of ER expression.
[0167] In this study, it was shown that expression of FOXC1, a
transcription factor essential for mesoderm tissue development in
vertebrates (Berry et al., 2002; Saleem et al., 2003) and a marker
for basal-like breast cancer (Ray et al., 2010), inversely
correlates with levels of ER.alpha. in breast cancer tissues and
cell lines. Specifically, it was found that FOXC1 upregulates the
NF-.kappa.B p65 subunit, which then downregulates ER.alpha.
expression via a transcriptional mechanism. Upregulation of p65
also desensitizes breast cancer cells to estradiol and the
antiestrogen tamoxifen. Essentially, FOXC1 overexpression causes
cells to switch from estrogen-dependent to NF-.kappa.B-dependent
proliferation, a finding confirmed by breast cancer cell
sensitivity to NF-.kappa.B inhibition. NF-.kappa.B is a
well-established transcription factor that plays a central role in
regulating the expression of many genes associated with cell
proliferation, immune response, inflammation, cell survival, and
oncogenesis (Karin et al., 2002; Lin et al.). This study provides
evidence for NF-.kappa.B-mediated crosstalk between ER.alpha. and
FOXC1.
[0168] Previous studies have revealed that forkhead box A1 (FOXA1)
and GATA binding protein 3 (GATA-3) are expressed in close
association with ER.alpha. (Albergaria et al., 2009). Both are
transcription factors implicated in ER.alpha.-mediated action in
breast cancer (Eeckhoute et al., 2007; Lupien et al., 2008; van der
Heul-Nieuwenhuijsen et al., 2009). FOXA1 binds to chromatin DNA and
opens the chromatin structure, thereby enhancing the binding of
ER.alpha. to the promoters of its target genes. The binding site of
FOXA1 is usually in close proximity to ER.alpha. binding sites. In
this regard, FOXA1 acts as a priming factor in the recruitment of
ER.alpha. to its cis-regulatory elements in the genome and
subsequent transcriptional induction of target genes such as cyclin
D1 in breast cancer cells (Carroll et al., 2005; Laganiere et al.,
2005). GATA-3 is required for estrogen stimulation of cell cycle
progression in breast cancer cells. It upregulates ER.alpha. by
binding to two cis-regulatory elements located within the ER.alpha.
gene; this binding allows recruitment of RNA polymerase II to
ER.alpha. promoters (Eeckhoute et al., 2007). Another forkhead box
transcription factor FOXO3a also induces ER.alpha. expression via
binding to the ER.alpha. promoter (Belguise and Sonenshein, 2007;
Guo and Sonenshein, 2004).
[0169] In addition to its association with ER.alpha.-negative
breast cancer in general, NF-.kappa.B activation has been linked to
EGFR or HER-2 overexpression-induced loss of ER in inflammatory
breast cancer (Van Laere et al., 2007). This is consistent with an
earlier finding that NF-.kappa.B mediates the downregulation of ER
by hyperactive MAPK (Holloway et al., 2004; Oh et al., 2001),
commonly induced by EGFR and HER-2 overexpression. It should be
noted that mechanisms for the inhibition of ER.alpha. by
NF-.kappa.B are still not well understood. NF-.kappa.B p65 may act
by directly binding to the ER.alpha. promoter (Mahmoodzadeh et al.,
2009; Tanimoto et al., 1999). In addition to the reported
NF-.kappa.B binding sites in the B promoter of the ER.alpha. gene
(Tanimoto et al., 1999), there is a highly conserved NF-.kappa.B
binding site in the F promoter of ER.alpha. at nucleotides -380 to
-420 (Mahmoodzadeh et al., 2009). CHIP analysis confirmed that
NF-.kappa.B can bind to the region containing the conserved
sequences. Another possibility is that p65 interacts with ER.alpha.
and thereby inhibits ER.alpha. activity (Gionet et al., 2009; Stein
and Yang, 1995). This may in turn reduce ER.alpha. transcription,
which can be positively regulated by estrogen-activated ER.alpha.
itself through half EREs in its promoter (Piva et al., 1988;
Treilleux et al., 1997). The NF-.kappa.B effect may also be
explained in part by its regulation of genes that modulate
ER.alpha. expression.
[0170] In summary, this study delineates a mechanism for the low or
absent ER.alpha. expression in basal-like breast cancer and
proposes a new paradigm for investigating the control of ER.alpha.
expression during breast cancer progression. These findings build
on a previous report that expression of cyclin D1 and other
growth-promoting genes is increased in FOXC1-overexpressing cells.
A link between ER.alpha. and FOXC1/NF-.kappa.B may have clinical
implications for ER.alpha.-positive breast cancer patients who
recur with ER.alpha.-negative cancer.
Example 6
Prognostic Stratification of HER2-Enriched Patients Utilizing
Semi-Quantitative Gene Expression Microarray Assessment of
FOXC1
[0171] Human epidermal growth factor receptor 2 (HER2) enriched
tumors display either gene amplification or protein overexpression.
This subtype of breast cancer is notable for its variable prognosis
and response to systemic therapy. It has been suggested that a
subset of HER2-positive tumors exhibit basal-like characteristics,
the so-called basal-HER2 subtype. The basal-HER2 subtype has been
shown to have the worst prognosis within HER2-overexpressing
tumors. It has been suggested that the basal-HER2 subtype
simultaneously co-expresses HER2 and markers typical of basal-like
breast cancer. As described in the examples above, FOXC1 is a
theranostic biomarker of the basal-like breast cancer molecular
subtype. Therefore, FOXC1 expression may be investigated within
HER2-overexpressing tumors to determine whether FOXC1 expression
represents the basal-HER2 subtype and prognosticates poor overall
survival (OS).
[0172] Gene expression microarray data from 58 HER2-amplified
tumors were obtained from a publicly available database that
contained linked clinical outcomes data (J Clin Oncol. 2010 Apr.
10; 28(11):1813-20. Epub 2010 Mar. 15). The data was analyzed for
FOXC1 expression and a median cutoff value (50th percentile) was
used to segregate tumors into FOXC1 high and FOXC1 low
designations. Prognostic significance of FOXC1 (high vs. low) was
assessed using univariate and multivariate analyses.
[0173] FIG. 14 shows that the FOXC1 high designation had a
significantly worse OS compared to the FOXC1 low designation
(p=0.0313). FOXC1 high designation was an independent prognostic
indicator for worse OS when controlled for age, tumor size, and
lymph node status (HR 2.54, 95% CI 1.21-5.33, p=0.0138).
[0174] Tumors that co-express FOXC1 and HER2 may represent the
hybrid basal-like/HER2+ subtype. Patients with HER2-enriched tumors
that have an elevated FOXC1 expression display worse survival.
Assessment of FOXC1 expression within HER2-enriched tumors may
represent a pragmatic approach for the diagnosis and prognosis of
the basal-HER2 subtype.
Example 7
Prognostic Stratification of Luminal Patients Utilizing
Semi-Quantitative Gene Expression Microarray Assessment of
FOXC1
[0175] Estrogen receptor and/or progesterone receptor-enriched
tumors display either gene amplification or protein overexpression
of ER and/or PR. A subset of ER-positive and/or PR positive tumors
may exhibit basal-like characteristics, the so-called hybrid
basal-like/luminal subtype. The hybrid basal-like/luminal subtype
likely has the worst prognosis within ER or PR overexpressing
tumors. The hybrid basal-like/luminal subtype likely simultaneously
co-expresses ER and/or PR and markers typical of basal-like breast
cancer. As described in the examples above, FOXC1 is a theranostic
biomarker of the basal-like breast cancer molecular subtype.
Therefore, FOXC1 expression may be investigated within ER and/or PR
overexpressing tumors to determine whether FOXC1 expression
represents the hybrid basal-like/luminal subtype and prognosticates
poor overall survival (OS).
[0176] Gene expression microarray data from ER and/or PR amplified
tumors may be obtained from a publicly available database that
contains linked clinical outcomes data. The data may be analyzed
for FOXC1 expression and a median cutoff value should be used to
segregate tumors into FOXC1 high and FOXC1 low designations.
Prognostic significance of FOXC1 (high vs. low) may be assessed
using univariate and multivariate analyses.
[0177] FOXC1 high designation likely has a significantly worse OS
compared to the FOXC1 low designation within the luminal subtype.
FOXC1 high designation is likely an independent prognostic
indicator for worse OS when controlled for age, tumor size, and
lymph node status.
[0178] Tumors that co-express FOXC1 and ER and/or PR may represent
the hybrid basal-like/luminal subtype. Patients with ER and/or PR
enriched tumors that have an elevated FOXC1 expression are likely
to display worse survival. Assessment of FOXC1 expression within ER
and/or PR enriched tumors may represent a pragmatic approach for
the diagnosis and prognosis of the hybrid basal-like/luminal
subtype.
Example 8
Prognostic Stratification of Triple Negative Patients Utilizing
Semi-Quantitative Gene Expression Microarray Assessment of
FOXC1
[0179] Triple negative tumors do not express ER, PR or HER2. A
subset of triple-negative tumors may exhibit basal-like
characteristics, the so-called hybrid basal-like/triple negative
subtype. The hybrid basal-like/triple negative subtype is
associated with the worst prognosis within triple negative tumors.
The hybrid basal-like/triple negative subtype likely expresses
markers typical of basal-like breast cancer. As described in the
examples above, FOXC1 is a theranostic biomarker of the basal-like
breast cancer molecular subtype. Therefore, FOXC1 expression may be
investigated within triple negative tumors to determine whether
FOXC1 expression represents the hybrid basal-like/triple negative
subtype and prognosticates poor overall survival (OS).
[0180] Gene expression microarray data from triple negative tumors
may be obtained from a publicly available database that contains
linked clinical outcomes data. The data may be analyzed for FOXC1
expression and a median cutoff value should be used to segregate
tumors into FOXC1 high and FOXC1 low designations. Prognostic
significance of FOXC1 (high vs. low) may be assessed using
univariate and multivariate analyses.
[0181] FOXC1 high designation likely has a significantly worse OS
compared to the FOXC1 low designation within the triple negative
subtype. FOXC1 high designation is likely an independent prognostic
indicator for worse OS when controlled for age, tumor size, and
lymph node status.
[0182] Tumors that express FOXC1 and not ER, PR and HER2 may
represent the hybrid basal-like/triple negative subtype. Patients
with triple negative tumors that have an elevated FOXC1 expression
are likely to display worse survival. Assessment of FOXC1
expression within triple negative tumors may represent a pragmatic
approach for the diagnosis and prognosis of the hybrid
basal-like/luminal subtype.
REFERENCES
[0183] The references listed below, and all references cited in the
specification are hereby incorporated by reference in their
entirety. [0184] Akaike H. A new look at the statistical model
identification. IEEE Trans Automatic Control 1974; 19:716-23.
[0185] Akaogi K, Nakajima Y, Ito I, Kawasaki S, Oie S H, Murayama A
et al (2009). KLF4 suppresses estrogen-dependent breast cancer
growth by inhibiting the transcriptional activity of ERalpha.
Oncogene 28: 2894-902. [0186] Albergaria A, Paredes J, Sousa B,
Milanezi F, Carneiro V, Bastos J et al (2009). Expression of FOXA1
and GATA-3 in breast cancer: the prognostic significance in hormone
receptor-negative tumours. Breast Cancer Res 11: R40. [0187] 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. [0188]
Aslakson C J, Miller F R. Selective events in the metastatic
process defined by analysis of the sequential dissemination of
subpopulations of a mouse mammary tumor. Cancer Res 1992;
52:1399-405. [0189] Belguise K, Sonenshein G E (2007). PKCtheta
promotes c-Rel-driven mammary tumorigenesis in mice and humans by
repressing estrogen receptor alpha synthesis. J Clin Invest 117:
4009-21. [0190] Berry F B, Saleem R A, Walter M A (2002). FOXC1
transcriptional regulation is mediated by N- and C-terminal
activation domains and contains a phosphorylated transcriptional
inhibitory domain. J Biol Chem 277: 10292-7. [0191] Berry F B,
Mirzayans F, Walter M A. Regulation of FOXC1 stability and
transcriptional activity by an epidermal growth factor-activated
mitogen-activated protein kinase signaling cascade. J Biol Chem
2006; 281:10098-104. [0192] Biswas D K, Shi Q, Baily S, Strickland
I, Ghosh S, Pardee A B et al (2004). NF-kappa B activation in human
breast cancer specimens and its role in cell proliferation and
apoptosis. Proc Natl Acad Sci USA 101: 10137-42. [0193] Biswas D K,
Singh S, Shi Q, Pardee A B, Iglehart J D (2005). Crossroads of
estrogen receptor and NF-kappaB signaling. Sci STKE 2005: pe27.
[0194] Bloushtain-Qimron N, Yao J, Snyder E L, et al. Cell
type-specific DNA methylation patterns in the human breast. Proc
Natl Acad Sci USA 2008; 105:14076-81. [0195] Carey L A, Perou C M,
Livasy C A, et al. Race, breast cancer subtypes, and survival in
the Carolina Breast Cancer Study. JAMA 2006; 295:2492-502. [0196]
Carroll J S, Liu X S, Brodsky A S, Li W, Meyer C A, Szary A J et al
(2005). Chromosome-wide mapping of estrogen receptor binding
reveals long-range regulation requiring the forkhead protein FoxA1.
Cell 122: 33-43. [0197] Charafe-Jauffret E, Monville F, Bertucci F,
et al. Moesin expression is a marker of basal breast carcinomas.
Int J Cancer 2007; 121: 1779-85. [0198] Cheang M C, Voduc D, Bajdik
C, Leung S, McKinney S, Chia S K, et al. Basal-like breast cancer
defined by five biomarkers has superior prognostic value than
triple-negative phenotype. Clin Cancer Res. 2008 Mar. 1;
14(5):1368-76. [0199] Couse J F, Korach K S (1999). Estrogen
receptor null mice: what have we learned and where will they lead
us? Endocr Rev 20: 358-417. [0200] Cui X, Zhang P, Deng W,
Oesterreich S, Lu Y, Mills G B et al (2003). Insulin-like growth
factor-I inhibits progesterone receptor expression in breast cancer
cells via the phosphatidylinositol 3-kinase/Akt/mammalian target of
rapamycin pathway: progesterone receptor as a potential indicator
of growth factor activity in breast cancer. Mol Endocrinol 17:
575-88. [0201] Cox D R H D. Theoretical Statistics. New York, N.Y.,
Chapman and Hall, 1974. 1974. [0202] deConinck E C, McPherson L A,
Weigel R J (1995). Transcriptional regulation of estrogen receptor
in breast carcinomas. Mol Cell Biol 15: 2191-6. [0203] Dent R,
Trudeau M, Pritchard K I, et al. Triple-negative breast cancer:
clinical features and patterns of recurrence. Clin Cancer Res 2007;
13:4429-34. [0204] Dhasarathy A, Kajita M, Wade P A (2007). The
transcription factor snail mediates epithelial to mesenchymal
transitions by repression of estrogen receptor-alpha. Mol
Endocrinol 21: 2907-18. [0205] Eeckhoute J, Keeton E K, Lupien M,
Krum S A, Carroll J S, Brown M (2007). Positive cross-regulatory
loop ties GATA-3 to estrogen receptor alpha expression in breast
cancer. Cancer Res 67: 6477-83. [0206] Elsheikh S E, Green A R,
Rakha E A, et al. Caveolin 1 and caveolin 2 are associated with
breast cancer basal-like and triple-negative immunophenotype. Br J
Cancer 2008; 99:327-34. [0207] Farmer P, Bonnefoi H, Becette V, et
al. Identification of molecular apocrine breast tumours by
microarray analysis. Oncogene 2005; 24:4660-71. [0208] Fuqua S A,
Schiff R, Parra I, Moore J T, Mohsin S K, Osborne C K et al (2003).
Estrogen receptor beta protein in human breast cancer: correlation
with clinical tumor parameters. Cancer Res 63: 2434-9. [0209]
Ginestier C, Cervera N, Finetti P, Esteyries S, Esterni B, Adelaide
J et al (2006). Prognosis and gene expression profiling of
20q13-amplified breast cancers. Clin Cancer Res 12: 4533-44. [0210]
Gionet N, Jansson D, Mader S, Pratt M A (2009). NF-kappaB and
estrogen receptor alpha interactions: Differential function in
estrogen receptor-negative and -positive hormone-independent breast
cancer cells. J Cell Biochem 107: 448-59. [0211] Green S, Walter P,
Kumar V, Krust A, Bornert J M, Argos P et al (1986). Human
oestrogen receptor cDNA: sequence, expression and homology to
v-erb-A. Nature 320: 134-9. [0212] Guo S, Sonenshein G E (2004).
Forkhead box transcription factor FOXO3a regulates estrogen
receptor alpha expression and is repressed by the
Her-2/neu/phosphatidylinositol 3-kinase/Akt signaling pathway. Mol
Cell Biol 24: 8681-90. [0213] Hasegawa M, Moritani S, Murakumo Y,
et al. CD109 expression in basal-like breast carcinoma. Pathol Int
2008; 58: 288-94. [0214] Herschkowitz J I, Simin K, Weigman V J, et
al. Identification of conserved gene expression features between
murine mammary carcinoma models and human breast tumors. Genome
Biol 2007; 8:R76. [0215] Hess K R, Anderson K, Symmans W F, et al.
Pharmacogenomic predictor of sensitivity to preoperative
chemotherapy with paclitaxel and fluorouracil, doxorubicin, and
cyclophosphamide in breast cancer. J Clin Oncol 2006; 24:4236-44.
[0216] Holloway J N, Murthy S, El-Ashry D (2004). A cytoplasmic
substrate of mitogen-activated protein kinase is responsible for
estrogen receptor-alpha down-regulation in breast cancer cells: the
role of nuclear factor-kappaB. Mol Endocrinol 18: 1396-410. [0217]
Nosey A M, Gorski J J, Murray M M, Quinn J E, Chung W Y, Stewart G
E et al (2007). Molecular basis for estrogen receptor alpha
deficiency in BRCA1-linked breast cancer. J Natl Cancer Inst 99:
1683-94. [0218] Hu Z, Fan C, Oh D S, et al. The molecular portraits
of breast tumors are conserved across microarray platforms. BMC
Genomics 2006; 7:96. [0219] Ihemelandu C U, Leffall L D, Jr.,
Dewitty R L, Naab T J, Mezghebe H M, Makambi K H, et al. Molecular
breast cancer subtypes in premenopausal and postmenopausal
African-American women: age-specific prevalence and survival. J
Surg Res. 2007 November; 143(1):109-18. [0220] Ihemelandu C U, Naab
T J, Mezghebe H M, Makambi K H, Siram S M, Leffall L D, Jr., et al.
Treatment and survival outcome for molecular breast cancer subtypes
in black women. Ann Surg. 2008 March; 247(3):463-9. [0221] Ivshina
A V, George J, Senko O, et al. Genetic reclassification of
histologic grade delineates new clinical subtypes of breast cancer.
Cancer Res 2006; 66:10292-301. [0222] Karin M, Cao Y, Greten F R,
Li Z W (2002). NF-kappaB in cancer: from innocent bystander to
major culprit. Nat Rev Cancer 2: 301-10. [0223] Keen J C, Davidson
N E (2003). The biology of breast carcinoma. Cancer 97: 825-33.
[0224] Korsching E, Jeffrey S S, Meinerz W, Decker T, Boecker W,
Buerger H. Basal carcinoma of the breast revisited: an old entity
with new interpretations. J Clin Pathol 2008; 61: 553-60. [0225]
Kos M, Reid G, Denger S, Gannon F (2001). Minireview: genomic
organization of the human ERalpha gene promoter region. Mol
Endocrinol 15: 2057-63. [0226] 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. [0227] Kuiper G G, Carlsson B,
Grandien K, Enmark E, Haggblad J, Nilsson S et al (1997).
Comparison of the ligand binding specificity and transcript tissue
distribution of estrogen receptors alpha and beta. Endocrinology
138: 863-70. [0228] Laganiere J, Deblois G, Lefebvre C, Bataille A
R, Robert F, Giguere V (2005). From the Cover: Location analysis of
estrogen receptor alpha target promoters reveals that FOXA1 defines
a domain of the estrogen response. Proc Natl Acad Sci USA 102:
11651-6. [0229] Landis S H, Murray T, Bolden S, Wingo P A (1999).
Cancer statistics, 1999. CA Cancer J Clin 49: 8-31, 1. [0230] Lin
Y, Bai L, Chen W, Xu S The NF-kappaB activation pathways, emerging
molecular targets for cancer prevention and therapy. Expert Opin
Ther Targets 14: 45-55. [0231] Lin Z, Yin P, Reierstad S,
O'Halloran M, Coon V J, Pearson E K et al Adenosine A1 receptor, a
target and regulator of estrogen receptoralpha action, mediates the
proliferative effects of estradiol in breast cancer. Oncogene 29:
1114-22. [0232] Livasy C A, Karaca G, Nanda R, et al. Phenotypic
evaluation of the basal-like subtype of invasive breast carcinoma.
Mod Pathol 2006; 19:264-71. [0233] Lu S, Simin K, Khan A, Mercurio
A M. Analysis of integrin beta4 expression in human breast cancer:
association with basal-like tumors and prognostic significance.
Clin Cancer Res 2008; 14: 1050-8. [0234] Lu X, Wang Z C, Iglehart J
D, Zhang X, Richardson A L (2008). Predicting features of breast
cancer with gene expression patterns. Breast Cancer Res Treat 108:
191-201. [0235] Lupien M, Eeckhoute J, Meyer C A, Wang Q, Zhang Y,
Li W et al (2008). FoxA1 translates epigenetic signatures into
enhancer-driven lineage-specific transcription. Cell 132: 958-70.
[0236] Mahmoodzadeh S, Fritschka S, Dworatzek E, Pham T H, Becher
E, Kuehne A et al (2009). Nuclear factor-kappaB regulates estrogen
receptor-alpha transcription in the human heart. J Biol Chem 284:
24705-14. [0237] Mani S A, Yang J, Brooks M, et al. Mesenchyme
Forkhead 1 (FOXC2) plays a key role in metastasis and is associated
with aggressive basal-like breast cancers. Proc Natl Acad Sci USA
2007; 104: 10069-74. [0238] McShane L M, Altman D G, Sauerbrei W,
Taube S E, Gion M, Clark G M. REporting recommendations for tumour
MARKer prognostic studies (REMARK). Br J Cancer. 2005 Aug. 22;
93(4):387-91. [0239] Miller L D, Smeds J, George J, et al. An
expression signature for p53 status in human breast cancer predicts
mutation status, transcriptional effects, and patient survival.
Proc Natl Acad Sci USA 2005; 102:13550-5. [0240] Moyano J V, Evans
J R, Chen F, et al. AlphaB-crystallin is a novel oncoprotein that
predicts poor clinical outcome in breast cancer. J Clin Invest
2006; 116: 261-70. [0241] Nakshatri H, Bhat-Nakshatri P, Martin D
A, Goulet R J, Jr., Sledge G W, Jr. (1997). Constitutive activation
of NF-kappaB during progression of breast cancer to
hormone-independent growth. Mol Cell Biol 17: 3629-39. [0242]
Nielsen T O, Hsu F D, Jensen K, et al. Immunohistochemical and
clinical characterization of the basal-like subtype of invasive
breast carcinoma. Clin Cancer Res 2004; 10:5367-74. [0243]
Nishimura D Y, Swiderski R E, Alward W L, Searby C C, Patil S R,
Bennet S R et al (1998). The forkhead transcription factor gene
FKHL7 is responsible for glaucoma phenotypes which map to 6p25. Nat
Genet 19: 140-7. [0244] Oh A S, Lorant L A, Holloway J N, Miller D
L, Kern F G, El-Ashry D (2001). Hyperactivation of MAPK induces
loss of ERalpha expression in breast cancer cells. Mol Endocrinol
15: 1344-59. [0245] Osborne C K (1998). Steroid hormone receptors
in breast cancer management. Breast Cancer Res Treat 51: 227-38.
[0246] Park W C, Jordan V C (2002). Selective estrogen receptor
modulators (SERMS) and their roles in breast cancer prevention.
Trends Mol Med 8: 82-8. [0247] Parker J S, Mullins M, Cheang M C,
et al. Supervised risk predictor of breast cancer based on
intrinsic subtypes. J Clin Oncol 2009; 27: 1160-7. [0248] Pawitan
Y, Bjohle J, Amler L, et al. Gene expression profiling spares early
breast cancer patients from adjuvant therapy: derived and validated
in two population-based cohorts. Breast Cancer Res 2005; 7:
R953-64. [0249] Perou C M, Sorlie T, Eisen M B, et al. Molecular
portraits of human breast tumours. Nature 2000; 406:747-52. [0250]
Piva R, Bianchini E, Kumar V L, Chambon P, del Senno L (1988).
Estrogen induced increase of estrogen receptor RNA in human breast
cancer cells. Biochem Biophys Res Commun 155: 943-9. [0251] Pollack
J R, Sorlie T, Perou C M, Rees C A, Jeffrey S S, Lonning P E et al
(2002). Microarray analysis reveals a major direct role of DNA copy
number alteration in the transcriptional program of human breast
tumors. Proc Natl Acad Sci USA 99: 12963-8. [0252] Qu Y, Wang J,
Sim M S, Liu B, Giuliano A, Barsoum J et al (2009). Elesclomol,
counteracted by Akt survival signaling, enhances the apoptotic
effect of chemotherapy drugs in breast cancer cells. Breast Cancer
Res Treat. [0253] Ray P, Wang J, Qu Y, Sim M, Shamonki J, Bagaria S
P et al (2010). FOXC1 Is a Potential Prognostic Biomarker with
Functional Significance in Basal-like Breast Cancer. Cancer
Research May 15. [0254] Rakha E A, Elsheikh S E, Aleskandarany M A,
Habashi H O, Green A R, Powe D G, et al. Triple-negative breast
cancer: distinguishing between basal and nonbasal subtypes. Clin
Cancer Res. 2009 Apr. 1; 15(7):2302-10. [0255] Richardson A L, Wang
Z C, De Nicolo A, et al. X chromosomal abnormalities in basal-like
human breast cancer. Cancer Cell 2006; 9:121-32. [0256] Rosen E M,
Fan S, Isaacs C (2005). BRCA1 in hormonal carcinogenesis: basic and
clinical research. Endocr Relat Cancer 12: 533-48. [0257] Ryo A,
Suizu F, Yoshida Y, Perrem K, Liou Y C, Wulf G et al (2003).
Regulation of NF-kappaB signaling by Pin1-dependent prolyl
isomerization and ubiquitin-mediated proteolysis of p65/RelA. Mol
Cell 12: 1413-26. [0258] Saceda M, Grunt T W, Colomer R, Lippman M
E, Lupu R, Martin M B (1996). Regulation of estrogen receptor
concentration and activity by an erbB/HER ligand in breast
carcinoma cell lines. Endocrinology 137: 4322-30. [0259] Saleem R
A, Banerjee-Basu S, Berry F B, Baxevanis A D, Walter M A (2003).
Structural and functional analyses of disease-causing missense
mutations in the forkhead domain of FOXC1. Hum Mol Genet 12:
2993-3005. [0260] Sarrio D, Rodriguez-Pinilla S M, 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. [0261] Schuetz C S, Bonin M, Clare S E,
Nieselt K, Sotlar K, Walter M et al (2006). Progression-specific
genes identified by expression profiling of matched ductal
carcinomas in situ and invasive breast tumors, combining laser
capture microdissection and oligonucleotide microarray
analysis.
Cancer Res 66: 5278-86. [0262] Seewaldt V L, Scott V. Images in
clinical medicine. Rapid progression of basal-type breast cancer. N
Engl J Med 2007; 356:e12. [0263] Shirley S H, Rundhaug J E, Tian J,
Cullinan-Ammann N, Lambertz I, Conti C J et al (2009).
Transcriptional regulation of estrogen receptor-alpha by p53 in
human breast cancer cells. Cancer Res 69: 3405-14. [0264] Singh S,
Shi Q, Bailey S T, Palczewski M J, Pardee A B, Iglehart J D et al
(2007). Nuclear factor-kappaB activation: a molecular therapeutic
target for estrogen receptor-negative and epidermal growth factor
receptor family receptor-positive human breast cancer. Mol Cancer
Ther 6: 1973-82. [0265] Sorlie T, Perou C M, Tibshirani R, Aas T,
Geisler S, Johnsen H et al (2001). Gene expression patterns of
breast carcinomas distinguish tumor subclasses with clinical
implications. Proc Natl Acad Sci USA 98: 10869-74. [0266] Sorlie T,
Tibshirani R, Parker J, et al. Repeated observation of breast tumor
subtypes in independent gene expression data sets. Proc Natl Acad
Sci USA 2003; 100:8418-23. [0267] Staaf J, Ringner M,
Vallon-Christersson J, Jonsson G, Bendahl P O, Holm K, Arason A,
Gunnarsson H, Hegardt C, Agnarsson B A, Luts L, Grabau D, Ferno M,
Malmstrom P O, Johannsson O T, Loman N, Barkardottir R B, Borg A.
Identification of subtypes in human epidermal growth factor
receptor 2-positive breast cancer reveals a gene signature
prognostic of outcome. J Clin Oncol. 2010 Apr. 10; 28(11):1813-20.
[0268] Stein B, Yang M X (1995). Repression of the interleukin-6
promoter by estrogen receptor is mediated by NF-kappa B and C/EBP
beta. Mol Cell Biol 15: 4971-9. [0269] Tanimoto K, Eguchi H,
Yoshida T, Hajiro-Nakanishi K, Hayashi S (1999). Regulation of
estrogen receptor alpha gene mediated by promoter B responsible for
its enhanced expressionin human breast cancer. Nucleic Acids Res
27: 903-9. [0270] Treilleux, Peloux N, Brown M, Sergeant A (1997).
Human estrogen receptor (ER) gene promoter-P1:
estradiol-independent activity and estradiol inducibility in ER+
and ER- cells. Mol Endocrinol 11: 1319-31. [0271] van der
Heul-Nieuwenhuijsen L, Dits N F, Jenster G (2009). Gene expression
of forkhead transcription factors in the normal and diseased human
prostate. BJU Int 103: 1574-80. [0272] van de Vijver M J, He Y D,
van't Veer L J, et al. A gene-expression signature as a predictor
of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.
[0273] Van Laere S J, Van der Auwera I, Van den Eynden G G, van Dam
P, Van Marck E A, Vermeulen P B et al (2007). NF-kappaB activation
in inflammatory breast cancer is associated with oestrogen receptor
downregulation, secondary to EGFR and/or ErbB2 overexpression and
MAPK hyperactivation. Br J Cancer 97: 659-69. [0274] Wang Y, Klijn
J G, Zhang Y, et al. Gene-expression profiles to predict distant
metastasis of lymph-node-negative primary breast cancer. Lancet
2005; 365:671-9. [0275] Yaziji H, Goldstein L C, Barry T S, Werling
R, Hwang H, Ellis G K, et al. HER-2 testing in breast cancer using
parallel tissue-based methods. JAMA. 2004 Apr. 28; 291 (16):1972-7.
[0276] Bland J M, Altman D G. Survival probabilities (the
Kaplan-Meier method). BMJ. 1998 Dec. 5; 317(7172):1572. [0277]
Yoshida T, Eguchi H, Nakachi K, Tanimoto K, Higashi Y, Suemasu K et
al (2000). Distinct mechanisms of loss of estrogen receptor alpha
gene expression in human breast cancer: methylation of the gene and
alteration of trans-acting factors. Carcinogenesis 21: 2193-201.
[0278] Zhao H, Langerod A, Ji Y, Nowels K W, Nesland J M,
Tibshirani R et al (2004). Different gene expression patterns in
invasive lobular and ductal carcinomas of the breast. Mol Biol Cell
15: 2523-36. [0279] Zhao J J, Lin J, Yang H, Kong W, He L, Ma X et
al (2008). MicroRNA-221/222 negatively regulates estrogen receptor
alpha and is associated with tamoxifen resistance in breast cancer.
J Biol Chem 283: 31079-86.
Sequence CWU 1
1
14157DNAArtificial SequenceMouse FoxC1 shRNA sequence 1ccgggagcag
agctactatc gcgctctcga gagcgcgata gtagctctgc tcttttg
57258DNAArtificial SequenceMouse FoxC1 shRNA sequence 2ccggtgggaa
tagtagctgt cagatctcga gatctgacag ctactattcc catttttg
58358DNAArtificial SequenceHuman FoxC1 shRNA sequence 3ccggcaagaa
gaaggacgcg gtgaactcga gttcaccgcg tccttcttct tgtttttg
58457DNAArtificial SequenceHuman FoxC1 shRNA sequence 4ccggcccgga
caagaagatc accctctcga gagggtgatc ttcttgtccg ggttttt
57557DNAArtificial SequenceControl shRNA 5ccggcaacaa gatgaagagc
accaactcga gttggtgctc ttcatcttgt tgttttt 57625DNAArtificial
SequenceFOXC1 forward primer 6cggtatccag ccagtctctg taccg
25721DNAArtificial SequenceFOXC1 reverse primer 7gttcggcttt
gagggtgtgt c 21825DNAArtificial SequenceFOXC1 ERalpha forward
primer 8cggttagatt catcatgcgg aaccg 25921DNAArtificial
SequenceFOXC1 ERalpha reverse primer 9tgtgtagagg gcatggtgga g
211020DNAArtificial SequenceChIP assay ERalpha forward primer
10agaagctaga cctctgcagg 201121DNAArtificial SequenceChIP assay
ERalpha reverse prime 11aagcaggggc aaggaaatat c
21123946DNAArtificial SequenceFOXC1/FKHL7 gene 12cgagaaaagg
tgacgcgggg cccgggcagg cggccggcgc gcggcccccc ccccccccgc 60cctggttatt
tggccgcctt cgccggcagc tcagggcaga gtctcctgga aggcgcaggc
120agtgtggcga gaagggcgcc tgcttgttct ttctttttgt ctgctttccc
ccgtttgcgc 180ctggaagctg cgccgcgagt tcctgcaagg cggtctgccg
cggccgggcc cggccttctc 240ccctcgcagc gaccccgcct cgcggccgcg
cgggccccga ggtagcccga ggcgccggag 300gagccagccc cagcgagcgc
cgggagaggc ggcagcgcag ccggacgcac agcgcagcgg 360gccggcacca
gctcggccgg gcccggactc ggactcggcg gccggcgcgg cgcggcccgg
420cccgagcgag ggtggggggc ggcgggcggc gcggggcggc ggcgagcggg
ggccatgcag 480gcgcgctact ccgtgtccag ccccaactcc ctgggagtgg
tgccctacct cggcggcgag 540cagagctact accgcgcggc ggccgcggcg
gccgggggcg gctacaccgc catgccggcc 600cccatgagcg tgtactcgca
ccctgcgcac gccgagcagt acccgggcgg catggcccgc 660gcctacgggc
cctacacgcc gcagccgcag cccaaggaca tggtgaagcc gccctatagc
720tacatcgcgc tcatcaccat ggccatccag aacgccccgg acaagaagat
caccctgaac 780ggcatctacc agttcatcat ggaccgcttc cccttctacc
gggacaacaa gcagggctgg 840cagaacagca tccgccacaa cctctcgctc
aacgagtgct tcgtcaaggt gccgcgcgac 900gacaagaagc cgggcaaggg
cagctactgg acgctggacc cggactccta caacatgttc 960gagaacggca
gcttcctgcg gcggcggcgg cgcttcaaga agaaggacgc ggtgaaggac
1020aaggaggaga aggacaggct gcacctcaag gagccgcccc cgcccggccg
ccagcccccg 1080cccgcgccgc cggagcaggc cgacggcaac gcgcccggtc
cgcagccgcc gcccgtgcgc 1140atccaggaca tcaagaccga gaacggtacg
tgcccctcgc cgccccagcc cctgtccccg 1200gccgccgccc tgggcagcgg
cagcgccgcc gcggtgccca agatcgagag ccccgacagc 1260agcagcagca
gcctgtccag cgggagcagc cccccgggca gcctgccgtc ggcgcggccg
1320ctcagcctgg acggtgcgga ttccgcgccg ccgccgcccg cgccctccgc
cccgccgccg 1380caccatagcc agggcttcag cgtggacaac atcatgacgt
cgctgcgggg gtcgccgcag 1440agcgcggccg cggagctcag ctccggcctt
ctggcctcgg cggccgcgtc ctcgcgcgcg 1500gggatcgcac ccccgctggc
gctcggcgcc tactcgcccg gccagagctc cctctacagc 1560tccccctgca
gccagacctc cagcgcgggc agctcgggcg gcggcggcgg cggcgcgggg
1620gccgcggggg gcgcgggcgg cgccgggacc taccactgca acctgcaagc
catgagcctg 1680tacgcggccg gcgagcgcgg gggccacttg cagggcgcgc
ccgggggcgc gggcggctcg 1740gccgtggaca accccctgcc cgactactct
ctgcctccgg tcaccagcag cagctcgtcg 1800tccctgagtc acggcggcgg
cggcggcggc ggcgggggag gccaggaggc cggccaccac 1860cctgcggccc
accaaggccg cctcacctcg tggtacctga accaggcggg cggagacctg
1920ggccacttgg caagcgcggc ggcggcggcg gcggccgcag gctacccggg
ccagcagcag 1980aacttccact cggtgcggga gatgttcgag tcacagagga
tcggcttgaa caactctcca 2040gtgaacggga atagtagctg tcaaatggcc
ttcccttcca gccagtctct gtaccgcacg 2100tccggagctt tcgtctacga
ctgtagcaag ttttgacaca ccctcaaagc cgaactaaat 2160cgaaccccaa
agcaggaaaa gctaaaggaa cccatcaagg caaaatcgaa actaaaaaaa
2220aaaaatccaa ttaaaaaaaa cccctgagaa tattcaccac accagcgaac
agaatatccc 2280tccaaaaatt cagctcacca gcaccagcac gaagaaaact
ctattttctt aaccgattaa 2340ttcagagcca cctccacttt gccttgtcta
aataaacaaa cccgtaaact gttttataca 2400gagacagcaa aatcttggtt
tattaaagga cagtgttact ccagataaca cgtaagtttc 2460ttcttgcttt
tcagagacct gctttcccct cctcccgtct cccctctctt gccttcttcc
2520ttgcctctca cctgtaagat attattttat cctatgttga agggaggggg
aaagtccccg 2580tttatgaaag tcgctttctt tttattcatg gacttgtttt
aaaatgtaaa ttgcaacata 2640gtaatttatt tttaatttgt agttggatgt
cgtggaccaa acgccagaaa gtgttcccaa 2700aacctgacgt taaattgcct
gaaactttaa attgtgcttt ttttctcatt ataaaaaggg 2760aaactgtatt
aatcttattc tatcctcttt tctttctttt tgttgaacat attcattgtt
2820tgtttattaa taaattacca ttcagtttga atgagaccta tatgtctgga
tactttaata 2880gagctttaat tattacgaaa aaagatttca gagataaaac
actagaagtt acctattctc 2940cacctaaatc tctgaaaaat ggagaaaccc
tctgactagt ccatgtcaaa ttttactaaa 3000agtctttttg tttagattta
ttttcctgca gcatcttctg caaaatgtac tatatagtca 3060gcttgctttg
aggctagtaa aaagatattt ttctaaacag attggagttg gcatataaac
3120aaatacgttt tctcactaat gacagtccat gattcggaaa ttttaagccc
atgaatcagc 3180cgcggtctta ccacggtgat gcctgtgtgc cgagagatgg
gactgtgcgg ccagatatgc 3240acagataaat atttggcttg tgtattccat
ataaaattgc agtgcatatt atacatccct 3300gtgagccaga tgctgaatag
attttttcct attatttcag tcctttataa aaggaaaaat 3360aaaccagttt
ttaaatgtat gtatataatt ctcccccatt tacaatcctt catgtattac
3420atagaaggat tgctttttta aaaatatact gcgggttgga aagggatatt
taatctttga 3480gaaactattt tagaaaatat gtttgtagaa caattatttt
tgaaaaagat ttaaagcaat 3540aacaagaagg aaggcgagag gagcagaaca
ttttggtcta gggtggtttc tttttaaacc 3600attttttctt gttaatttac
agttaaacct aggggacaat ccggattggc cctccccctt 3660ttgtaaataa
cccaggaaat gtaataaatt cattatctta gggtgatctg ccctgccaat
3720cagactttgg ggagatggcg atttgattac agacgttcgg gggggtgggg
ggcttgcagt 3780ttgttttgga gataatacag tttcctgcta tctgccgctc
ctatctagag gcaacactta 3840agcagtaatt gctgttgctt gttgtcaaaa
tttgatcatt gttaaaggat tgctgcaaat 3900aaatacactt taatttcagt
caaaaaaaaa aaaaaaaaaa aaaaaa 3946131659DNAArtificial
SequenceFOXC1/FKHL7 gene coding sequence 13atgcaggcgc gctactccgt
gtccagcccc aactccctgg gagtggtgcc ctacctcggc 60ggcgagcaga gctactaccg
cgcggcggcc gcggcggccg ggggcggcta caccgccatg 120ccggccccca
tgagcgtgta ctcgcaccct gcgcacgccg agcagtaccc gggcggcatg
180gcccgcgcct acgggcccta cacgccgcag ccgcagccca aggacatggt
gaagccgccc 240tatagctaca tcgcgctcat caccatggcc atccagaacg
ccccggacaa gaagatcacc 300ctgaacggca tctaccagtt catcatggac
cgcttcccct tctaccggga caacaagcag 360ggctggcaga acagcatccg
ccacaacctc tcgctcaacg agtgcttcgt caaggtgccg 420cgcgacgaca
agaagccggg caagggcagc tactggacgc tggacccgga ctcctacaac
480atgttcgaga acggcagctt cctgcggcgg cggcggcgct tcaagaagaa
ggacgcggtg 540aaggacaagg aggagaagga caggctgcac ctcaaggagc
cgcccccgcc cggccgccag 600cccccgcccg cgccgccgga gcaggccgac
ggcaacgcgc ccggtccgca gccgccgccc 660gtgcgcatcc aggacatcaa
gaccgagaac ggtacgtgcc cctcgccgcc ccagcccctg 720tccccggccg
ccgccctggg cagcggcagc gccgccgcgg tgcccaagat cgagagcccc
780gacagcagca gcagcagcct gtccagcggg agcagccccc cgggcagcct
gccgtcggcg 840cggccgctca gcctggacgg tgcggattcc gcgccgccgc
cgcccgcgcc ctccgccccg 900ccgccgcacc atagccaggg cttcagcgtg
gacaacatca tgacgtcgct gcgggggtcg 960ccgcagagcg cggccgcgga
gctcagctcc ggccttctgg cctcggcggc cgcgtcctcg 1020cgcgcgggga
tcgcaccccc gctggcgctc ggcgcctact cgcccggcca gagctccctc
1080tacagctccc cctgcagcca gacctccagc gcgggcagct cgggcggcgg
cggcggcggc 1140gcgggggccg cggggggcgc gggcggcgcc gggacctacc
actgcaacct gcaagccatg 1200agcctgtacg cggccggcga gcgcgggggc
cacttgcagg gcgcgcccgg gggcgcgggc 1260ggctcggccg tggacaaccc
cctgcccgac tactctctgc ctccggtcac cagcagcagc 1320tcgtcgtccc
tgagtcacgg cggcggcggc ggcggcggcg ggggaggcca ggaggccggc
1380caccaccctg cggcccacca aggccgcctc acctcgtggt acctgaacca
ggcgggcgga 1440gacctgggcc acttggcaag cgcggcggcg gcggcggcgg
ccgcaggcta cccgggccag 1500cagcagaact tccactcggt gcgggagatg
ttcgagtcac agaggatcgg cttgaacaac 1560tctccagtga acgggaatag
tagctgtcaa atggccttcc cttccagcca gtctctgtac 1620cgcacgtccg
gagctttcgt ctacgactgt agcaagttt 165914553PRTArtificial
SequenceFOXC1/FKHL7 protein sequence 14Met Gln Ala Arg Tyr Ser Val
Ser Ser Pro Asn Ser Leu Gly Val Val1 5 10 15Pro Tyr Leu Gly Gly Glu
Gln Ser Tyr Tyr Arg Ala Ala Ala Ala Ala 20 25 30Ala Gly Gly Gly Tyr
Thr Ala Met Pro Ala Pro Met Ser Val Tyr Ser 35 40 45His Pro Ala His
Ala Glu Gln Tyr Pro Gly Gly Met Ala Arg Ala Tyr 50 55 60Gly Pro Tyr
Thr Pro Gln Pro Gln Pro Lys Asp Met Val Lys Pro Pro65 70 75 80Tyr
Ser Tyr Ile Ala Leu Ile Thr Met Ala Ile Gln Asn Ala Pro Asp 85 90
95Lys Lys Ile Thr Leu Asn Gly Ile Tyr Gln Phe Ile Met Asp Arg Phe
100 105 110Pro Phe Tyr Arg Asp Asn Lys Gln Gly Trp Gln Asn Ser Ile
Arg His 115 120 125Asn Leu Ser Leu Asn Glu Cys Phe Val Lys Val Pro
Arg Asp Asp Lys 130 135 140Lys Pro Gly Lys Gly Ser Tyr Trp Thr Leu
Asp Pro Asp Ser Tyr Asn145 150 155 160Met Phe Glu Asn Gly Ser Phe
Leu Arg Arg Arg Arg Arg Phe Lys Lys 165 170 175Lys Asp Ala Val Lys
Asp Lys Glu Glu Lys Asp Arg Leu His Leu Lys 180 185 190Glu Pro Pro
Pro Pro Gly Arg Gln Pro Pro Pro Ala Pro Pro Glu Gln 195 200 205Ala
Asp Gly Asn Ala Pro Gly Pro Gln Pro Pro Pro Val Arg Ile Gln 210 215
220Asp Ile Lys Thr Glu Asn Gly Thr Cys Pro Ser Pro Pro Gln Pro
Leu225 230 235 240Ser Pro Ala Ala Ala Leu Gly Ser Gly Ser Ala Ala
Ala Val Pro Lys 245 250 255Ile Glu Ser Pro Asp Ser Ser Ser Ser Ser
Leu Ser Ser Gly Ser Ser 260 265 270Pro Pro Gly Ser Leu Pro Ser Ala
Arg Pro Leu Ser Leu Asp Gly Ala 275 280 285Asp Ser Ala Pro Pro Pro
Pro Ala Pro Ser Ala Pro Pro Pro His His 290 295 300Ser Gln Gly Phe
Ser Val Asp Asn Ile Met Thr Ser Leu Arg Gly Ser305 310 315 320Pro
Gln Ser Ala Ala Ala Glu Leu Ser Ser Gly Leu Leu Ala Ser Ala 325 330
335Ala Ala Ser Ser Arg Ala Gly Ile Ala Pro Pro Leu Ala Leu Gly Ala
340 345 350Tyr Ser Pro Gly Gln Ser Ser Leu Tyr Ser Ser Pro Cys Ser
Gln Thr 355 360 365Ser Ser Ala Gly Ser Ser Gly Gly Gly Gly Gly Gly
Ala Gly Ala Ala 370 375 380Gly Gly Ala Gly Gly Ala Gly Thr Tyr His
Cys Asn Leu Gln Ala Met385 390 395 400Ser Leu Tyr Ala Ala Gly Glu
Arg Gly Gly His Leu Gln Gly Ala Pro 405 410 415Gly Gly Ala Gly Gly
Ser Ala Val Asp Asn Pro Leu Pro Asp Tyr Ser 420 425 430Leu Pro Pro
Val Thr Ser Ser Ser Ser Ser Ser Leu Ser His Gly Gly 435 440 445Gly
Gly Gly Gly Gly Gly Gly Gly Gln Glu Ala Gly His His Pro Ala 450 455
460Ala His Gln Gly Arg Leu Thr Ser Trp Tyr Leu Asn Gln Ala Gly
Gly465 470 475 480Asp Leu Gly His Leu Ala Ser Ala Ala Ala Ala Ala
Ala Ala Ala Gly 485 490 495Tyr Pro Gly Gln Gln Gln Asn Phe His Ser
Val Arg Glu Met Phe Glu 500 505 510Ser Gln Arg Ile Gly Leu Asn Asn
Ser Pro Val Asn Gly Asn Ser Ser 515 520 525Cys Gln Met Ala Phe Pro
Ser Ser Gln Ser Leu Tyr Arg Thr Ser Gly 530 535 540Ala Phe Val Tyr
Asp Cys Ser Lys Phe545 550
* * * * *
References