Identification and testing of a gene expression signature of invasive carcinoma cells within primary mammary tumors

Article (PDF Available)inCancer Research 64(23):8585-94 · January 2005with34 Reads
DOI: 10.1158/0008-5472.CAN-04-1136 · Source: PubMed
Abstract
We subjected cells collected using an in vivo invasion assay to cDNA microarray analysis to identify the gene expression profile of invasive carcinoma cells in primary mammary tumors. Expression of genes involved in cell division, survival, and cell motility were most dramatically changed in invasive cells indicating a population that is neither dividing nor apoptotic but intensely motile. In particular, the genes coding for the minimum motility machine that regulates beta-actin polymerization at the leading edge and, therefore, the motility and chemotaxis of carcinoma cells, were dramatically up-regulated. However, ZBP1, which restricts the localization of beta-actin, the substrate for the minimum motility machine, was down-regulated. This pattern of expression implicated ZBP1 as a suppressor of invasion. Reexpression of ZBP1 in metastatic cells with otherwise low levels of ZBP1 reestablished normal patterns of beta-actin mRNA targeting and suppressed chemotaxis and invasion in primary tumors. ZBP1 reexpression also inhibited metastasis from tumors. These experiments support the involvement in metastasis of the pathways identified in invasive cells, which are regulated by ZBP1.
[CANCER RESEARCH 64, 8585– 8594, December 1, 2004]
Identification and Testing of a Gene Expression Signature of Invasive Carcinoma
Cells within Primary Mammary Tumors
Weigang Wang, Sumanta Goswami, Kyle Lapidus, Amber L. Wells, Jeffrey B. Wyckoff, Erik Sahai,
Robert H. Singer, Jeffrey E. Segall, and John S. Condeelis
Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York
ABSTRACT
We subjected cells collected using an in vivo invasion assay to cDNA
microarray analysis to identify the gene expression profile of invasive
carcinoma cells in primary mammary tumors. Expression of genes in-
volved in cell division, survival, and cell motility were most dramatically
changed in invasive cells indicating a population that is neither dividing
nor apoptotic but intensely motile. In particular, the genes coding for the
minimum motility machine that regulates
-actin polymerization at the
leading edge and, therefore, the motility and chemotaxis of carcinoma
cells, were dramatically up-regulated. However, ZBP1, which restricts the
localization of
-actin, the substrate for the minimum motility machine,
was down-regulated. This pattern of expression implicated ZBP1 as a
suppressor of invasion. Reexpression of ZBP1 in metastatic cells with
otherwise low levels of ZBP1 reestablished normal patterns of
-actin
mRNA targeting and suppressed chemotaxis and invasion in primary
tumors. ZBP1 reexpression also inhibited metastasis from tumors. These
experiments support the involvement in metastasis of the pathways iden-
tified in invasive cells, which are regulated by ZBP1.
INTRODUCTION
Understanding how cancer cells spread from the primary tumor is
important for improving diagnostic, prognostic, and therapeutic ap-
proaches that allow control of cancer metastasis. Many of the form-
ative steps that determine the invasive and metastatic potential of
carcinoma cells occur within the primary tumor. Much evidence
suggests that the progress of cells from normal to invasive and then to
metastatic involves progressive transformation through multiple ge-
netic alterations selected by the tumor microenvironment (1). To
identify the steps in progression and the genes involved in metastasis,
recent emphasis has been on the use of molecular arrays to identify
expression signatures in whole tumors with differing metastatic po-
tential (2). A well-recognized problem here is that primary tumors
show extensive variation in properties with different regions of the
tumor having different growth, histology, and metastatic potential and
where only a small subset of cells within the parental tumor popula-
tion may be capable of metastasizing (3). The array data derived from
whole tumors results inevitably in averaging of the expression of
different cell types from all of these diverse regions. The expression
signature of invasive tumor cells, arguably the population essential for
metastasis, may be masked or even lost because of the contribution of
surrounding cells, which represent the bulk of the tumor mass. Even
so, recent studies of expression profiling of primary tumors suggest
that the metastatic potential of tumors is encoded in the bulk of a
primary tumor, thus challenging the notion that metastases arise from
rare cells within a primary tumor acquired late during tumor progres-
sion (4).
This leaves us with a conundrum concerning the contribution of
rare cells to the metastatic phenotype. The relative contribution
of subpopulations of cells to the invasive and metastatic phenotype of
primary tumors has not been assessed due to the difficulty in isolating
phenotypically distinct cell populations from whole tumors. In addi-
tion, the metastatic cascade has been studied most heavily at the level
of extravasation and beyond using experimental metastasis models
removing the primary tumor from scrutiny (5). Thus, the microenvi-
ronment of the primary tumor that contributes to invasion and intrav-
asation and the process of selection of metastatic cells has not been
studied directly.
In this context it has become important to develop technologies to
separate pure populations of invasive cancer cells for gene expression
studies. To this end, the development of laser capture microdissection
has been an important advance (6). However, the identification of
cells within the tumor relies on morphology within fixed tissue
making uncertain the identity of the collected cells and their behavior
within the tumor before fixation. Alternative approaches involve the
collection of cells from metastatic tumors and their expansion in
culture (7–9). The pitfall of these approaches is that during culturing,
the gene expression patterns may change to represent the in vitro
culture conditions, which are likely to be irrelevant to invasion in vivo.
Another approach in determining the cellular mechanisms that
contribute to invasion is to collect live cells from the primary tumor
based on their ability to invade and profile their gene expression
patterns. One of the properties correlated with metastasis is chemo-
taxis to blood vessels (10). This cell behavior allows cells to orient
and move toward blood vessels facilitating their intravasation. On the
basis of these observations, we have developed an in vivo invasion
assay capable of collecting live invasive cells from live primary
tumors in intact animals using chemotaxis to growth factors (11). We
have used the in vivo invasion assay to test the hypothesis that
chemotaxis to blood vessels is an important form of egress of carci-
noma cells from the primary tumor. Cells have been collected from
live rats with tumors of different metastatic potential (11) and from
live mice with mammary tumors derived from the expression of the
PyMT oncogene (12–14).
To perform gene expression profiling using high density arrays on
the few hundred cells commonly collected in the in vivo invasion
assay, it is necessary to amplify mRNA by 1,000-fold to the
amounts required for arrays. It is also necessary to have a pure cell
population. Both of these conditions have been met using methods
developed recently (14). RNA obtained from as few as 400 cells
collected in a single microneedle from the primary tumor, when
amplified as cDNA using the PCR-based cDNA amplification tech-
nique (15), can be used for microarray expression analysis. This
amplification method was validated and demonstrated to retain the
original mRNA copy abundance and complexity in the amplified
product (14).
In the current study, the collection of invasive cells from the
primary tumor using chemotaxis to epidermal growth factor (EGF) in
Received 3/30/04; revised 9/3/04; accepted 9/24/04.
Grant support: NIH [GM25813, CA100324 (J. S. Condeelis), and CA0893208 (R. H.
Singer)]. E. Sahai is funded by a Unio Internationale Contra Cancrum Aventis Transla-
tional Cancer Research Fellowship.
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked advertisement in accordance with
18 U.S.C. Section 1734 solely to indicate this fact.
Note: Supplementary data for this article can be found at Cancer Research Online
(http://cancerres.aacrjournals.org).
Requests for reprints: Weigang Wang, Department of Anatomy and Structural
Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY
10461. Phone: 718-430-4461; Fax: 718-430-8996; E-mail: wgwang@aecom.yu.edu.
©2004 American Association for Cancer Research.
8585
the in vivo invasion assay was combined with gene expression pro-
filing using these amplification techniques. This technology has al-
lowed the characterization of gene expression patterns of invasive
carcinoma cells from the primary tumor without potential artifacts,
which arise from the culturing of small populations of cells. We
identified a group of genes that define motility pathways that are
coordinately up-regulated in invasive cells. These pathways may
account for the enhanced migratory behavior of the collected cells.
Furthermore, we tested the contribution of these pathways to invasion
and metastasis by altering the expression of a master gene that
regulates the expression of the common molecule on which these
pathways converge.
MATERIALS AND METHODS
In vivo Invasion Assay and Fluorescence-Activated Cell Sorting of
Primary Tumor Cells. We used MTLn3-derived mammary tumors in rats
(16) and the in vivo invasion assay described previously (11, 14) to study the
gene expression pattern of invasive subpopulations of carcinoma cells within
live primary tumors. Briefly, the in vivo invasion assay uses microneedles
filled with Matrigel and growth factors to collect the invasive cells from
primary tumors. Microneedles are held in a clamping device and positioned in
the primary tumor with a micromanipulator. Cell collection takes from 1 to 4
hours and was imaged using a multiphoton microscope as described previously
(17) by inserting the bevel of a Matrigel and EGF-containing needle into the
field of view. A 50-
m z-series consisting of 5-
m steps allows for the
imaging of a large number of cells around the needle. One tenth of the volume
from each needle was used to determine the number of cells collected.
Collected cells were a mixture of carcinoma cells (75%) and macrophages
(25%). From the remaining 9/10 volume from the microneedle, macrophages
were removed by magnetic separation, and RNA was extracted from purified
carcinoma cells as described before (14). To isolate the general population of
carcinoma cells from primary tumor, a small piece of tumor was separated
from the whole tumor, minced, and filtered twice through a nylon filter to
obtain a single cell suspension. Fluorescence-activated cell sorting was per-
formed on the resulting single cell suspensions based on their green fluorescent
protein (GFP) expression in tumor cells. GFP-positive tumor cells were col-
lected into a tube and lysed directly for RNA extraction. All of the procedures
were done on ice or 4°C.
Because EGF and Matrigel are present in the needle, as a control experi-
ment, we identified genes of which the expression is altered by EGF or
Matrigel application. Carcinoma cells from the primary tumor were fluores-
cence-activated cell sorted as described above. The resulting cells were split
and plated on Mattek dishes covered with Matrigel (1:5) in the presence or
absence of EGF (1 nmol/L) for 4 hours at 37°C. The cells were then lysed
directly on the dish for total RNA extraction.
RNA Amplification, Probe Labeling, and Microarray Hybridization.
Common reference RNA standard was prepared by mixing RNA (Ambion,
Austin, TX) from rat liver, spleen, brain, and kidney at a 4:2:1:1 RNA weight
ratio, respectively. Reference RNA was used to generate probes as a control
channel in all of our microarray experiments, which allowed us to use one of
the channels as a hybridization control for all of the spots on the microarray.
The use of common reference RNA from the same species as the MTLn3 cells
allowed the same interspecies cross-hybridization as the background, allowing
us to use mouse cDNA microarray for our experiments. The common reference
RNA covers a very broad range of gene expression, provides a standard for
reducing variation in microarray experiments, and allows for more reliable
comparison of gene expression data within and between experiments (18, 19).
Mouse cDNA microarrays were obtained from the Albert Einstein College of
Medicine cDNA microarray facility (general information about the array
printing, quality control, and gene annotation listing are available online
1
).
Each slide contained an unbiased, random collection of 27,396 mouse cDNA
probe elements (500 –1500 bp each) from sequence-verified clone sets by
Incyte Genomics (Palo Alto, CA), National Cancer Institute, and Integrated
Molecular Analysis of Genomes and their Expression Consortium. Among the
27,396 genes, 47% of genes were annotated known genes.
Microarray analysis was performed in at least three independent repeats, as
described previously with minor modification (14). In brief, the RNA from
needle collection or fluorescence-activated cell sorting was then concentrated
by EtOH precipitation and redissolved in 3.5
L diethyl pyrocarbonate water.
The total RNA was reverse-transcribed directly using the SMART PCR cDNA
synthesis kit (Clontech, Palo Alto, CA) according to the manufacturer’s
protocol. After amplification, cDNAs were purified using the QIAquick PCR
Purification kit (Qiagen, Chatsworth, CA) and eluted with 10 m
M Tris (pH 8)-1
m
M EDTA buffer. Common reference RNA was also amplified using SMART
protocol and purified for all of the array hybridizations. Labeling was per-
formed using Label IT (Mirus) following the manufacturer’s instructions.
Briefly, labeling reactions were prepared by mixing 10X Mirus Labeling
Buffer A (10
L), purified cDNA (3.5
g), Cy5 (for experimental sample) and
Cy3 (for common reference control channel) dye (5
L) in a total volume of
100
L. After incubating the reaction mix at 37°C for 1 hour, the two resulting
probes were purified by passing through SigmaSpin columns followed by
Qiaquick columns. The purified Cy-3 and Cy-5 DNA probes were then
combined and concentrated using micron YM 50 columns. Details of slide
hybridization, washing, and image collection were described in previous
studies (14, 17).
Quality Control, Normalization, and Statistical Analysis of Microarray
Data. The scanned images were analyzed using the software Genepix (Axon
Instruments, Inc., Foster City, CA), and an absolute intensity value was
obtained for each of the channels for the reference RNA and the RNA derived
from the cells. The entire raw data set consisting of 27,000 data points was
filtered to accommodate a requirement of at least two good quality measure-
ments for each triplicate experiment. Values from only the good quality
measurements (where the signal strength was more than twice the SD of the
background plus the background) were considered for additional analysis. This
quality control filtration resulted in removal of 5,000 spots resulting in 22,000
good spots. Two types of normalization were performed routinely in tandem on
all of the experiments using the GeneSpring software package (Silicon Genet-
ics, Redwood City, CA). First, intensity-based normalization was performed,
which takes into consideration the overall signal strength of both channels and
normalizes the signal strength between all of the different chips, reducing the
chance of chip-to-chip variability due to the experiment being performed on
different days. Second, a reference channel-based normalization was per-
formed, which takes into consideration the reference channel (which in this
case is pooled reference RNA) and normalizes the values in all of the spots.
This reduces the chance of spot-to-spot variability. The final data were a result
of both these types of normalization.
To determine the signal-to-noise level for up-regulated and down-regulated
genes, we calculated the SD of the reference channel in all of the chips and
found it to be 0.18 and used five times SD as the cutoff, indicating a high level
of fidelity in our data above 2-fold (i.e., 5 0.18). In the genes where a single
replicate was flagged and the other two were good, the flagged value was
removed and replaced with an average of the other two good values (20).
Statistical analysis was performed using Student’s t test on each of the data sets
for each gene. A P value was generated for all of the data sets (n 3 for
general population and n 6 for invasive cells). Genes that were up- or
down-regulated in the arrays performed on control samples (fluorescence-
activated cell sorted cells, which were treated with Matrigel and EGF) were
removed from the final list of genes specific to the invasive subpopulation of
tumor cells.
Real-Time PCR Confirmation. To verify the data obtained from microar-
rays, quantitative reverse-transcription-PCR (RT-PCR) analysis of selected
overexpressed and underexpressed genes was performed by using the iCycler
Apparatus (Bio-Rad, Hercules, CA) with sequence-specific primer pairs for all
of the genes tested (see Supplementary Table 1 for primer sequences, amplicon
size, and melting temperature) as described previously (17). The SYBR Green
PCR Core Reagents system (Applied Biosystems, Foster City, CA) was used
for real-time monitoring of amplification.
Plasmid Construction, Cell Culture, Transfection, Infection, and Gen-
eration of ZBP1 Stable Expression Cell Lines. FLAG-ZBP1 (21) was
digested with BamHI/XbaI and inserted into the BamHI/XbaI sites of EGFP-C1
(Clontech). The EGFP-FLAG-ZBP1, which encodes a fusion protein, was then
isolated as Eco47III/XbaI restriction fragment, blunt ended, and inserted into a
1
Internet address: http://www.aecom.yu.edu/home/molgen/facilities.html.
8586
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
filled XhoI site of pMCSVneo (Clontech). This vector contains a viral pack-
aging signal, neomycin resistance gene, and the 5 and 3 long terminal repeats
from the murine porcine cytomegalovirus. As a result, the long terminal repeat
drives high-level constitutive expression of EGFP-FLAG-ZBP1 gene. PHOE-
NIX cells were cultured under standard conditions (22) and were transfected
with EGFP-FLAG-ZBP1 using FUGENE (Roche, Indianapolis, IN). Retroviral
supernatant was harvested and used to infect MTLn3 cells as described
previously (22). Stable MTLn3 cells were selected in the presence of neomycin.
-Actin Fluorescence In situ Hybridization. MTLn3-GFP and MTLn3-
ZBP1 cells were grown in
-modified Eagle’s medium containing 5% fetal
bovine serum and antibiotics (penicillin and streptomycin). Stable MTLn3-
ZBP1 cell lines were cultured in the same medium with neomycin to maintain
selection of the clones. Cells were grown to 60% to 70% confluency on
acid-washed coverslips, washed with PBS, fixed with 4% paraformaldehyde in
PBS/5 mmol/L MgCl
2
, and stored in 70% EtOH at 4°C. In situ hybridization
for
-actin mRNA was performed as described previously (23). Briefly, the
cells were rehydrated in PBS/5 mmol/L MgCl
2
, permeabilized with 0.5%
Triton X-100, incubated with Cy3-labeled probes specific for rat
-actin
mRNA, washed, and mounted in Prolong Antifade medium (Molecular Probes,
Portland, OR).
Quantitation of
-Actin mRNA Localization. The cytoplasmic distribu-
tion of
-actin mRNA within cells was determined using fluorescence in situ
hybridization and analyzed using two different methods. The first method
scored cells as being localized or nonlocalized based on a visual inspection of
the pattern of message distribution. Cells containing only perinuclear pools of
mRNA were scored as nonlocalizing cells. Cells having message at the
periphery or showing a nonperinuclear distribution were scored as localizing
cells. The second method plots the fluorescence intensity of the Cy3-labeled
-actin probes as a function of distance from the nucleus. Cell and nuclear
edges were traced, and the longest cytoplasmic distance from the nucleus to the
edge of the cell was calculated in pixels so that the farthest edge of the cell has
a value of 100, and 0 corresponds with the edge of the nucleus. All of the
distances within the cell are reported as relative to the longest distance from the
nucleus. The fluorescence intensity is reported as a percentage of the total
fluorescence intensity of the region analyzed. The ratio of fluorescence inten-
sity distribution plots of MTLn3-ZBP1 normalized to control MTLn3 cells
shows the differences in the
-actin mRNA distribution between the two
groups.
Cell Morphology. Round cells were defined as having a length to width
ratio (in pixels) of between 1 and 1.5. Cells with the ratio 1.5 were scored as
polarized. Statistical significance was determined using an unpaired Student’s
t test.
Microchemotaxis Chamber Assay. A 48-well microchemotaxis chamber
(Neuroprobe, Cabin John, MD) was used to study the chemotactic response to
EGF, following the manufacturer’s instructions and as described previously
(24).
Blood Burden, Single Cells in the Lung, and Metastases. MTLn3-ZBP1
or MTLn3-GFP cells were injected into the mammary fat pads of female Fisher
344 rats. Tumor cell blood burden was determined as described in a previous
study (10). After blood removal and euthanization of the rat, the lungs were
removed, and the visible metastatic tumors near the surface of the lungs were
counted. For measurement of metastases, excised lungs were placed in 3.7%
formaldehyde, mounted in paraffin, sectioned, and stained with H&E. Slices
were viewed using a 20 objective, and all of the metastases in a section
containing 5 cells were counted (10).
RESULTS
Gene Expression Patterns Unique to Invasive Tumor Cells.
GFP-labeled tumor cells were injected into rat mammary fat pads, and
primary tumors were allowed to grow for 2 to 2.5 weeks. To provide
insight into the pattern of gene expression associated with chemotactic
and invasive carcinoma cells in vivo, we compared the gene expres-
sion profile of the subpopulation of invasive tumor cells collected
from the primary tumor using the in vivo invasion assay with that of
the general population of GFP-expressing tumor cells sorted from the
whole primary tumor by fluorescence-activated cell sorting (Fig. 1B).
Hereafter, the former population of cells will be called the invasive
cells and the latter the general population. The invasive subpopulation
of tumor cells was collected into microneedles filled with EGF and
Matrigel that were held in the primary tumor for up to 4 hours as
described previously (11, 14). Overexpression of the EGF receptor
and other family members is correlated with poor prognosis (25).
Natural gradients of EGF receptor ligands could be generated by
diffusion from the blood as well as stromal cells in the tumor micro-
environment (26, 27). Therefore, we used gradients of EGF in the in
vivo invasion assay to collect invasive carcinoma cells as a physio-
logically relevant stimulus to mimic tumor cell chemotaxis and inva-
sion to blood vessels observed in vivo (28). The collection of the
invasive cells was monitored by imaging the GFP-expressing cells
with a multiphoton microscope as they migrated to the EGF contain-
ing microneedles (Fig. 1A). This allowed direct confirmation that
collection was due to cell migration and not a passive process.
The collected cells were a mixture of carcinoma cells (75%) and
macrophages (25%) as shown previously (14). Macrophages were
removed by binding to magnetic beads conjugated with anti-MAC-1,
giving a 96% pure population of carcinoma cells for analysis (14).
The general population of primary tumor cells was collected by
fluorescence-activated cell sorting and plated either on Matrigel or
Matrigel and EGF for 4 hours, the interval of time required for
microneedle collection, to mimic the collection conditions before
purification of the RNA. These controls were done to subtract patterns
of gene expression resulting from stimulating cells with Matrigel and
EGF and allowed identification of the gene expression pattern unique
to the invasive cells (Fig. 1B). The genes that were subtracted as part
of this control are shown in Supplementary Table 2.
Stringent quality control and statistical analysis were performed on
the datasets collected from the invasive and general population.
Firstly, data were only considered from those spots that had a signif-
icantly higher signal value than the background as defined in Mate-
rials and Methods. Those genes that did not meet these criteria in more
than one spot out of three were removed from the dataset. Student’s
t test was performed on the data sets to determine the level of
significance between the two datasets (Tables 1 and 2).
Differential gene expression analysis comparing the invasive and
general populations of tumor cells revealed 1,366 genes of known
functions that were differentially expressed by 2-fold compared
with 27,000 arrayed (Supplementary Table 3). As shown in Fig. 1C,
genes with known functions were divided into 10 different functional
categories based on definitions provided by the gene-ontology con-
sortium (29).
2
Briefly each of the annotated genes was put into the
functional category that best describes its function. To determine the
significance of changes in gene expression in each of the functional
categories of the genes represented in our arrays, the number of genes
regulated either up or down in the invasive cells for each functional
category was compared with all of the genes on the array and the
statistical significance calculated using significance analysis of mi-
croarrays, software that performs significance analysis to determine
changes between the samples and replicates (20). We also used
2
analysis to calculate the random occurrence of genes in any one of the
functional categories (A detailed table indicating each of the func-
tional categories and the number of genes in each of them that are
either up or down-regulated is given in Supplementary Table 4A). The
average P value for all of the 1,366 genes was 0.0136. Given a P value
like this, 367 genes would have been up- or down-regulated ran-
domly on a 27,000 gene array at the 2-fold cutoff used. The proba-
bility that this pattern shown in Fig. 1C, panel c is generated randomly
is very low, because the numbers of genes in each functional category
2
Internet address: http://www.geneontology.org.
8587
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
shown in Fig. 1C, panels b and c (Supplementary Table 4A), is not
equal.
Of these categories we have studied three in more detail. The genes
of which the expression is regulated up or down in the functional
category called cell cycle (Fig. 1C, panel c, #1) in the invasive cells
compared with the general population was informative. In particular,
genes that enhance cell proliferation were down, and those that repress
proliferation were up (Supplementary Table 4B) indicating that the
cell proliferation activity of invasive cells is repressed (30). Second,
the number of genes regulated in the category called apoptosis (Fig.
1C, panel c, #2) is significantly higher in invasive cells collected into
needles compared with the general population of tumor cells collected
by fluorescence-activated cell sorting from the whole primary tumor.
In particular, antiapoptotic genes are up, and proapoptotic genes are
down-regulated indicating a survival advantage for invasive cells (31).
Third, of particular relevance to the migratory behavior of invasive
cells, there is an increase in the number of regulated genes in the
cytoskeleton and extracellular matrix (Fig. 1C-c, #7; Table 1). In
particular, the genes coding for the minimum motility machine, i.e.,
the cofilin, capping protein and Arp2/3 pathways, that regulate
-actin
polymerization, and therefore the motility of carcinoma cells, were
dramatically up-regulated (Fig. 2). These pathways may account for
the enhanced migratory behavior of the collected cells. Changes in
these three categories of gene expression indicate that invasive cells
are neither dividing nor apoptotic but are intensely migratory.
Gene expression patterns of metastatic cells obtained in a previous
study comparing metastatic and nonmetastatic cell lines and tumors
derived from them (17), were compared with differences in the
patterns of gene expression between invasive and noninvasive tumor
cells of the primary tumor observed in this study. From these com-
parisons a set of common genes emerged that were changed in all of
the cases (Table 2). This pattern of genes represents a potential
invasion signature that may be common to all of the cells in the
primary tumor of heightened metastatic potential. This study demon-
strates the value of comparing cell lines and tumors of different
metastatic potential with invasive cells collected from primary tumors.
Genes Involved in Invasion. To be collected in the in vivo inva-
sion assay, carcinoma cells must be capable of moving toward and
crawling into the extracellular matrix of the microneedle within the
4-hour collection interval. If a cell moves 2 cell diameters during this
interval to gain entry to the microneedle it would have a minimum
speed of 0.2
m/min, similar to the velocity of carcinoma cells in
vitro. However, carcinoma cells move in the primary tumor at speeds
up to 10 times this minimum value (28) indicating that cells from
hundreds of microns away from the microneedle can be recruited for
collection and that the cells may penetrate the extracellular matrix in
the collecting microneedle. Consistent with this prediction is the
observation that carcinoma cells are found deep within the matrix of
the collecting microneedle indicating that cells have traveled hundreds
of microns during the collection interval indicating speeds much
greater than 0.2
m/min in vivo.
As shown in Table 1, based on the microarray analysis, many genes
Fig. 1. In vivo selection and gene expression analysis of the highly invasive subpopu-
lation of breast cancer cells collected by chemotaxis. A, multiphoton images of live cell
collection in a MTLn3-derived tumor. GFP-expressing carcinoma cells (green) are seen
moving toward the bevel (dashed line delineates edge) of a microneedle filled with
Matrigel and 25 nmol/L EGF. Purple lines are extracellular matrix of the primary tumor
imaged due to its second harmonic signal. Arrows indicate the final location of invading
cells in both frames over the time lapse interval of 1 hour. Scale bar 25
m. B,
schematic representation of the chemotaxis-based selection process. MTLn3-derived
mammary tumors in rats and the in vivo invasion assay were used to study the gene
expression pattern of the invasive subpopulation of carcinoma cells within live primary
tumors. Invasive cells were collected using the in vivo invasion assay (needle collection).
FACS based on GFP expression in tumor cells was performed to isolate the general
population of carcinoma cells from the primary tumor. RNA extraction, probe labeling,
and microarray analysis were carried out. Carcinoma cells from primary tumor were
fluorescence-activated cell sorted as described above. The resulting cells were split and
plated on a Mettek dish covered with Matrigel (1:5) in the presence (iv) or absence of 1
nmol/L EGF (iii) for 4 hours at 37°C. The cells were then lysed directly on the dish for
total RNA extraction, probe labeling, and microarray analysis. Genes that were up or
down-regulated on control experiments (comparison: iii versus ii and iv versus ii) were
removed from the list of differentially expressed genes obtained when comparing i and ii.
The resulting final list of 1,366 genes is shown in Supplementary Table 3. C, summary
diagram showing functional categories of the genes regulated in the invasive cells. These
pie charts represent the relative proportion of genes in 10 categories based on their
function using Gene-ontology Consortium classifications. Pie a represents the relative
proportion of annotated spots compared with expressed sequence tags on the array. Pie b
shows the proportional representation of the functional groups into which the genes
annotated in a fall. Pie c shows the proportional representation of the functional groups
into which the genes regulated in the invasive cells fall. (FACS, fluorescence-activated
cell sorting)
8588
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
associated with motility are up-regulated in the invasive cells com-
pared with the general population of cells. To make sense of Table 1
it is necessary to consider that the motility cycle of chemotactic
carcinoma cells is composed of five steps: signal sensing, protrusion
toward the signal source, adhesion, contraction, and tail retraction
(32). Of particular relevance to chemotaxis and invasion, the protru-
sion of a pseudopod toward the chemotactic signal initiating the
motility cycle is the key step in defining the leading edge of the cell
and, therefore, its direction during migration (32). Protrusion is driven
by actin polymerization-based pushing against the cell membrane, and
this requires the minimum motility machine composed of cofilin,
Arp2/3 complex, and capping protein acting on their common down-
stream effector,
-actin (33). The elevated expression of any one of
these three effectors is expected to significantly enhance the speed of
migration of cells, because doubling the amount of Arp2/3 complex,
capping protein, or cofilin in the reconstituted minimum motility
machine can increase protrusion rate by 10 times (34). Therefore, it is
significant, as shown in Fig. 2A, that the genes coding for all three of
the end-stage effectors, the Arp2/3 complex (the p16 and p21 sub-
units), capping protein, and cofilin, are up-regulated by at least 2-fold
each. Furthermore, the genes coding for the pathways regulating the
activities of Arp2/3 complex (WAVE3), capping protein, and cofilin
are coordinately up-regulated in the invasive cell population.
We verified, using real-time PCR, the array results for genes
detected to have changed expression in these pathways. As shown in
Fig. 2B, the same pattern of expression was observed in the invasive
cells with both microarray and real-time PCR analysis.
In the cofilin pathway, genes for ROCK1 and LIMK 1 representing
the inhibitory branch of the cofilin pathway are up-regulated. In
addition, cofilin and PKCz, representing the stimulatory side of the
cofilin pathway, are up-regulated. LIM kinase is activated by ROCK,
which is regulated by Rho-GTP. ROCK (35) can phosphorylate LIM
kinase thereby activating it to phosphorylate cofilin, which inhibits
cofilin. Inhibition of LIM kinase activity involves its phosphorylation
by the unconventional PKCz (36).
Similar increases in both the stimulatory and inhibitory parts of the
capping protein pathway are up-regulated in invasive carcinoma cells
(Fig. 2A). The expression of both the
and
subunits of capping
protein is increased. In addition, genes that antagonize capping protein
function such as the type II
isoform of PI4, 5 kinase, and Mena are
up-regulated (37, 38).
ZBP1 as a Master Gene Regulating Invasion and Metastasis. A
gene that is strongly down-regulated in invasive cells is Zip-code
binding protein (ZBP1; Tables 1 and 2; Fig. 2). ZBP1 is a 68-kDa
RNA-binding protein that binds to the mRNA zipcode of
-actin
mRNA and functions to localize the mRNA to the leading edge of
crawling cells.
-Actin is the preferred isoform of actin for the
polymerization of filaments at the leading edge of cells and, therefore,
is acted on by the cofilin, capping protein, and Arp2/3 pathways (39).
ZBP1 may determine the sites in cells where the Arp2/3 complex,
capping protein, and cofilin pathways converge by controlling the
sites of targeting of
-actin mRNA and the location of
-actin protein
that is the common downstream effector of these pathways (Fig. 2A).
-Actin mRNA localization is required for the generation of intrinsic
and stable cell polarity that is characteristic of normal primary fibro-
blasts and epithelial cells. Disruption of ZBP1-mediated
-actin
mRNA targeting leads to cells without intrinsic cell polarity, i.e., cells
that are more amoeboid (39). Loss of
-actin mRNA targeting is
correlated with the loss of intrinsic stable cell polarity, increased
amoeboid movement in metastatic carcinoma cell lines in vitro and in
vivo (17, 23), and increased chemotaxis. Therefore, ZBP1 expression
might suppress the chemotactic and invasive potential of carcinoma
Table 1 List of motility-related genes differentially expressed in the invasive subpopulation of tumor cells
Acc no. Gene description Invasive cells/general population P value
AA414612 Capping protein
1* 4.00 0.008
AW556230 Cell division cycle 42* 3.96 0.007
AU015486 Capping Protein
2 3.89 0.016
C79581 Moesin* 3.67 0.020
C86972 Arp 2/3 complex subunit p16* 3.52 0.011
AW538432 Rho interactin protein 3* 3.33 0.005
AU015879 LIM-kinase 1* 3.24 0.006
AA285584 Palladin 3.12 0.003
AW555565 Zyxin 2.93 0.014
W10023 Catenin
2.88 0.029
C76867 Tropomyosin
chain 2.86 0.001
AU023806 Rho-associated coiled-coil forming kinase 1* 2.71 0.002
AW536576 Testis expressed gene 9 2.67 0.006
AI324089 Phosphatidylinositol-4-phosphate 5-kinase type II
* 2.60 0.005
AI427644 Epidermal growth factor receptor* 2.59 0.019
AW541453 Capping protein (actin filament), gelsolin-like 2.53 0.020
C86107 Actinin
3* 2.52 0.003
AW543636 Annexin A5 2.47 0.013
AA052404 CRIPT protein 2.32 0.011
AA014771 Protein kinase C,
* 2.30 0.011
AW546733 Arp 2/3 complex subunit p21* 2.22 0.014
AA538228 RAB25, member RAS oncogene family 2.19 0.035
AA275245 Vinculin 2.16 0.021
AA386680 Kinesin family member 5B 2.13 0.015
AW536843 Chaperonin subunit 4 (
) 2.06 0.009
AW536183 Chaperonin subunit 3 (
) 2.06 0.042
AI326287 Tubulin
-4 chain 2.05 0.046
AW553280 Integrin
1 (fibronectin receptor
) 2.00 0.013
AW536098 Cofilin 1, nonmuscle* 2.00 0.023
AU017992 Kinectin 1 2.00 0.005
AW557123 Downstream of tyrosine kinase 1 2.00 0.013
AW549817 Burkitt lymphoma receptor 1 2.00 0.021
AA272097 Fibroblast growth factor receptor 1 0.54 0.004
AA073514 Zip code binding protein 1* 0.11 0.004
NOTE. Genes associated with motility are displayed in this table, and the ratios on the right indicate the level of expression in the invasive compared to the general population of
cells of the primary tumor. P values indicate the result of Student’s t test performed on these genes.
*These results have been validated by quantitative real-time–PCR.
8589
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
cells in tumors, which is proposed to require amoeboid movement and
chemotaxis.
To test the hypothesis that ZBP1 expression can suppress the
chemotaxis and invasion of cells in vivo, the full-length ZBP1 gene
was subcloned in a pMCSVneo vector (Fig. 3A) and transfected into
the parental MTLn3 cells. Data from Western blot analysis (Fig. 3B)
confirmed that stable clones transfected with pEGFP-FLAG-ZBP1
express higher levels of ZBP1 compared with untransfected cells. To
account for any effects that might arise from the introduction of EGFP
into cells, MTLn3 cells transfected with pGreenLantern-1 vector (Life
Technologies, Inc., Gaithersburg, MD) were used as control.
To investigate the effects of expressing ZBP1 in carcinoma cells on
-actin mRNA targeting, the ZBP1-expressing cells were analyzed
using fluorescence in situ hybridization. As shown in Fig. 4A—D, the
expression of ZBP1 in carcinoma cells rescued the targeting of
-actin
mRNA asymmetrically to a cell edge. Furthermore, cells expressing
ZBP1 became polarized (Fig. 4E).
To investigate the chemotactic properties of the ZBP1-expressing
cells, two independent clones of ZBP1-expressing cell lines were
characterized. Chemotaxis was measured in a Boyden chamber. ZBP-
1-expressing cells migrated through the filter in response to EGF
poorly compared with the parental MTLn3 cells (Fig. 5A), indicating
that chemotaxis was inhibited. This was true for both ZBP1 clones.
Furthermore, the ability of carcinoma cells to invade microneedles in
the in vivo invasion assay was greatly reduced for tumors derived
from MTLn3 cells expressing ZBP1 (Fig. 5B) indicating a reduction
in chemotaxis and invasion in vivo.
Injection of the ZBP1-expressing cells into the mammary fat pads
of rats resulted in tumors that were less metastatic. The metastatic
potential of these tumors was characterized as the efficiency of
intravasation, by measuring the number of tumor cells present in
circulating blood (Fig. 5C) and the number of spontaneously occur-
ring lung metastatic tumors (Fig. 5D). However, as shown in Fig. 5E,
tumor growth was not significantly affected by increasing the expres-
sion of ZBP1.
Fig. 3. ZBP1 construct and its expression in MTLn3 cells. A. The full-length ZBP1
gene was subcloned in a pMCSVneo vector and transfected into parental MTLn3 cells.
The control plasmid used in the experiments was the pGreenLantern-1. B. Stable MTLn3-
ZBP1 clones 1 and 11B were selected in the presence of neomycin. The Western blot
shows the increased ZBP1 protein expression in these two separate clones.
Fig. 2. Motility genes differentially expressed in invasive cells. A. The minimum
motility machine pathways in the invasive cells are up-regulated. Pathways leading to the
generation of protrusive force in response to EGF are shown. The extent of up-regulated
expression of differentially expressed genes is indicated next to each component of the
pathway as nX. The expression level of Mena shown here was calculated by quantitative
RT-PCR as 6.01.1. B, validation of microarray results for selected genes by QRT-PCR.
Expression analyses of invasive cells compared with the general population of cells in the
tumor by QRT-PCR gives the same pattern as did cDNA microarrays. (QRT-PCR,
quantitative real time PCR; CFL, cofilin; cap1, capping protein)
Table 2 Genes differentially expressed in invasive cells identified in this study showing the same pattern of expression in cell lines and metastatic tumors derived from them
Gene name Acc no.
Metastatic/nonmetastatic,
cell line*
Metastatic/nonmetastatic,
tumor† Needle/FACS‡ P value Gene function
Collagen, type III,
1 W89883 0.01 0.15 0.19 0.001 Extracellular matrix composition
G-protein coupled receptor 26 C77414 0.14 0.2 0.46 0.0002 Signal transduction
Zip code binding protein 1 AA073514 0.08 0.03 0.11 0.004 Cell polarity
Fibroblast growth factor receptor 1 AA272097 0.32 0.35 0.54 0.004 Signal transduction
ARP2/3 Complex 16 kD subunit C86972 6.31 5.45 3.52 0.011 Minimum motility machine
Tight junction protein 2 AU044024 2.96 2.16 3.4 0.019 Adhesion molecules
RAB25 member Ras oncogene family AA538228 7.99 6.38 2.18 0.017 Signal transduction
Epidermal growth factor receptor AI427644 20.12 2.0 2.6 0.019 Signal transduction
NOTE. Top part indicates lower expression and the lower part of the table represents overexpression. P values indicate the result of Student’s t test performed on these genes. Taken
together these genes outline a signature of invasion and indicate that a number of interacting pathways are involved in invasion.
Abbreviations: FACS, fluorescence-activated cell sorting.
*Metastatic cell line, MTLn3; nonmetastatic cell line, MTC.
†Tumor derived from injection of MTLn3 or MTC.
‡Needle, cells collected into needle by chemotaxis invasive; fluorescence-activated cell sorting, cells obtained from whole tumor by FACS general population.
8590
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
DISCUSSION
Patterns of Gene Expression in Invasive Carcinoma Cells. By
comparing gene expression patterns of invasive cells to those of the
general population of carcinoma cells in the same primary tumor, we
were able to find patterns in the regulation of gene expression unique
to the invasive subpopulation of cells. Our results indicate that the
invasive cells are a population that is neither proliferating nor apop-
totic but intensely motile. Whereas increased cell proliferation during
tumor development has been associated with poor prognosis in pa-
tients (40), the results reported both here and in previous studies (10)
indicate that tumor size is neither correlated with invasion nor the
ability of cells to metastasize to distant organs. In addition, invasive
cells show down-regulation of genes associated with apoptosis and
up-regulation of genes for cell survival (31). This is consistent with
previous work where it was shown that cell survival genes were
up-regulated in metastatic tumors as compared with nonmetastatic
tumors (17) and suggests that the invasive subpopulation may con-
tribute to this expression profile in whole metastatic tumors.
In addition, in this study we have identified a pattern of gene
expression common to metastatic cell lines and tumors and the inva-
sive population of cells in primary tumors (Table 2). This pattern of
genes represents a potential invasion signature that may be common to
all cells in the primary tumor of heightened metastatic potential. This
suggests that the invasive population of cells has enhanced an expres-
sion pattern of a subset of genes that is characteristic of the differences
between metastatic and nonmetastatic cell lines and tumors. This is
emphasized by the fact that the invasive subpopulation of cells col-
lected using the in vivo invasion assay is from tumors derived initially
from common genetic background, i.e., a single cell line, the MTLn3.
This indicates that as the tumor progresses, highly invasive cells are
selected in which a pattern of gene expression present in metastatic
cells and tumors is enhanced over the pattern of expression of the cells
that remain behind in the primary tumor. This pattern may represent
a signature for invasion that may be general to a number of carcino-
mas.
Recently, several studies of human primary breast tumors have
generated gene expression signatures that are predictive of metastasis
and poor survival (4, 41). Among the genes identified in our study
comparing invasive cells to the general population (Supplementary
Table 2), only SNRPF (small nuclear ribonucleoprotein F) and
LMNB1 encoding Lamin B1 were identified in common out of 8 of the
signature genes highly expressed in solid tumors in humans associat-
ing with metastasis and poor clinical outcome (4). None of the genes
found in common with metastatic cell lines and tumors (Table 2) were
represented in the gene expression patterns predicting poor survival in
these studies. Additional work with invasive cells collected from a
variety of human tumors will be required to evaluate the generality of
our invasion signature.
Cell Motility Genes and Their Roles in Cancer Invasion. Che-
motaxis and cell migration in response to EGF is correlated with
invasion, intravasation, and metastasis in animal models of breast
cancer (10, 11, 28). A major result of this study is the finding that
genes of the minimum motility machine are up-regulated, predicting
that protrusion velocity will be increased. Because the site of protru-
sion sets cell direction and, therefore, defines chemotaxis, this step in
length of the cell so that the mRNA distribution extends past the perinuclear region almost
reaching the far end of the cell (see B). E. The cell morphology of ZBP1 reexpressing cells
is more polarized. MTLn3 cells grown continuously in serum show heterogeneity in cell
shape with 50% of the cells rounded and the other 50% polarized. The percentage of
polarized cells in MTLn3-ZBP1 cells increases to 80%, which is a 1.6-fold increase
compared with parental MTLn3 cells (P 0.01, Student’s t test).
Fig. 4. ZBP1 reexpressing cells localize
-actin mRNA asymmetrically to the cell
periphery and are polarized. In situ hybridization using cy3-labeled probes against rat
-actin mRNA was used to show the distribution of message within cells grown contin-
uously in serum. Both parental MTLn3 (A) and MTLn3-ZBP1 (B) cells have perinuclear
pools of
-actin mRNA. However, ZBP1 reexpression increases the localization of
-actin mRNA to the cell periphery (B). Scale bar 10
m. C. MTLn3-ZBP1 reex-
pressing cells show a 1.6-fold increase in localized
-actin mRNA to the cell periphery
compared with parental cells (P 0.01, Student’s t test). Bars, SD. D. The localization
of
-actin mRNA in parental and ZBP1 reexpressing cells to the cell periphery was
quantified by plotting the location of mRNA as a function of distance from the nucleus.
MTLn3-ZBP1 reexpressing cells localize
-actin mRNA to more distal regions of the cell
than parental cells do. ZBP1 reexpression changes the message distribution along the
8591
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
the motility cycle may be a key in determining invasive potential.
These results are consistent with the 10-fold higher velocity of cell
migration toward blood vessels and EGF-filled microneedles, both
sources of chemoattractant, observed in primary tumors of live rats
and mice compared with the velocity of movement of their cultured
cell counterparts (10, 11, 16, 17, 28). Consistent with these results is
the finding that inhibition of the nucleation activity of Arp2/3 com-
plex in carcinoma cells in culture inhibits chemotaxis to EGF (42) and
that cofilin activity is required for cell motility (43), cell direction
(44), and chemotaxis (45) in carcinoma cells.
As seen in Fig. 2A, genes coding for key components of the
pathways regulating the minimum motility machine are coordinately
up-regulated. Both the stimulatory and inhibitory parts of the cofilin
and capping protein pathways are up-regulated. This is consistent with
the importance of transients of actin polymerization in the chemotaxis
of crawling cells (45, 46). If actin polymerization is either sustained or
inhibited, cell motility ceases. By up-regulating both the stimulatory
and inhibitory parts of pathways leading to actin polymerization, actin
polymerization occurs as a transient (47) allowing the cell to adjust
and move according to directional signals (45).
In previous studies, LIM kinase 1 was shown to be overexpressed
in metastatic breast and prostate tumors (48, 49). Overexpression of
LIM kinase 1 in tumor cell lines increased their motility and inva-
siveness in vitro (48) and in vivo (49). Reduction in the expression of
LIM kinase 1 in metastatic prostate cell lines decreased invasiveness
in Matrigel invasion assays (48). These results are consistent with ours
shown here that LIM kinase 1 is more highly expressed in the invasive
cell population.
However, it has also been reported that increased expression of
LIM kinase 1 in carcinoma cells significantly reduces their cell
motility, because the phosphorylation of cofilin by LIM kinase 1
abolishes EGF-induced actin nucleation and polymerization (50). Our
study may resolve this paradox by demonstrating that in invasive cells
collected from primary tumors both the stimulatory and inhibitory
pathways to LIM kinase 1 and cofilin are overexpressed together
thereby increasing the rate of cofilin activation and inactivation in
invasive carcinoma cells resulting in actin polymerization transients
and enhanced cell motility as predicted previously (48 –51) and ob-
served experimentally (44, 45).
Among the genes up-regulated in the pathways of Fig. 2, several
have been implicated in invasion and metastasis in previous studies.
Clinical studies of bladder cancer, breast cancer, and colorectal cancer
have implicated Rock (52), Mena (53), and the Arp2 and 3 subunits of
the Arp2/3 complex (54), respectively, as up-regulated in these can-
cers.
ZBP1 in Metastasis. ZBP1 is required for the targeting of
-actin
mRNA asymmetrically to the cell edge, and this helps to define an
intrinsic and stable cell polarity. Cells that lack an intrinsic and stable
polarity are more chemotactic to exogenous gradients presumably
because there is no intrinsic polarity to be overcome by the exogenous
chemotactic signal, and the cell can turn in any direction to respond to
the gradient (55, 56). In carcinoma cells, an EGF gradient generates
transients of actin polymerization that lead to transient cell polarity
toward the source of the gradient (45). Therefore, in tumors, cells that
have proceeded through the epithelial mesenchymal transition to the
point where all remnants of the intrinsic and stable cell polarity of the
original epithelium are lost are predicted be more efficient at respond-
ing to external chemotactic signals. This may account for the en-
hanced ability of carcinoma cells to chemotax to blood vessels and to
intravasate in metastatic primary tumors (10, 28).
In the present study, invasive tumor cells isolated from primary
mammary tumors using chemotaxis express much lower levels of
ZBP1 than cells that remain behind in the primary tumor, although
both cell populations were derived from the same progenitor MTLn3
cells (Tables 1 and 2). Furthermore, as shown in the current study,
reexpression of ZBP1 rescues both
-actin mRNA targeting and
Fig. 5. ZBP1 expression inhibits tumor invasion
and metastasis. A. ZBP1 expression inhibits cell
motility. Chemotaxis was measured in a Boyden
chamber. ZBP1-expressing cells migrated through
the filter in response to EGF poorly compared with
the parental MTLn3 cells B. ZBP1 expression in-
hibits chemotaxis and invasion in vivo as confirmed
by the in vivo invasion assay. The ability of carci-
noma cells to invade microneedles placed into pri-
mary tumors derived from MTLn3 cells expressing
ZBP1 was greatly reduced. Needles that do not
contain EGF represent background. C and D.
ZBP1-expressing cells show lower metastatic po-
tential. The number of tumor cells present in cir-
culating blood (C) and the number of lung meta-
static tumors (D) were greatly reduced in animals
with tumors prepared with cells expressing ZBP1
(P 0.05, by Mann-Whitney Test). However, as
shown in E, tumor growth was not affected by
increasing the expression of ZBP1; bars, SD.
8592
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
intrinsic cell polarization. This is correlated with a reduction in
chemotaxis, intravasation, and metastasis.
ZBP1 is a member of a family of RNA binding proteins expressed
in embryonic and transformed cells. All of these proteins contain four
COOH-terminal hnRNP K homology domains and two NH
2
-terminal
RNA recognition motifs (57). Expression of members of this protein
family occurs during development and becomes undetectable after
birth but is elevated in a variety of human cancers (57). Consistent
with this pattern of expression is our result that nonmetastatic cells
and tumors express detectable levels of ZBP1 (17). Overexpression of
a mouse homologue of ZBP1 called CRD-BP causes mammary tu-
mors in transgenic mice (58). However, lung metastases were not
detected in the CRD-BP-induced tumors. This is consistent with our
results reported here that ZBP1 expression suppresses lung metastasis
and that invasive and metastatic cells and tumors exhibit low levels of
ZBP1 expression compared with nonmetastatic cells and tumors (Ta-
ble 2). The ability of CRD-BP to induce tumor formation suggests
additional roles for this family of RNA binding proteins in tumori-
genesis beyond its demonstrated suppression of metastasis. One pos-
sible role is the ability of the CRD-BP to stabilize c-myc mRNA in
cells in culture (59).
The results reported here indicate that ZBP1 is a “metastasis sup-
pressor” and, together with mRNA targeting status and analysis of
tumor cell polarity around blood vessels discussed above, might be
used to predict the metastatic potential of mammary tumors.
ACKNOWLEDGMENTS
We thank Dr. Erwin Bottinger and the staff of Albert Einstein Biotechnol-
ogy Center for their invaluable help and expertise. We are grateful to Aldo
Massimi (Albert Einstein College of Medicine Microarray Core) and members
of the Analytical Imaging Facility for support and advice. We thank Alex
Rodriguez and Shailesh Shenoy for their essential contributions to the fluo-
rescence in situ hybridization analysis.
REFERENCES
1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100:57–70.
2. Liotta LA, Kohn EC. The microenvironment of the tumour-host interface. Nature
(Lond) 2001;411:375–9.
3. Fidler IJ, Kripke ML. Metastasis results from preexisting variant cells within a
malignant tumor. Science (Wash DC) 1977;197:893–5.
4. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis
in primary solid tumors. Nat Genet 2003;33:49 –54.
5. Chambers AF, Groom AC, MacDonald IC. Dissemination and growth of cancer cells
in metastatic sites. Nat Rev Cancer 2002;2:563–72.
6. Bonner RF, Emmert-Buck M, Cole K, et al. Laser capture microdissection: molecular
analysis of tissue. Science (Wash DC) 1997;278:1481,1483.
7. Clark EA, Golub TR, Lander ES, Hynes RO. Genomic analysis of metastasis reveals
an essential role for RhoC. Nature (Lond) 2000;406:532–5.
8. Kang Y, Siegel PM, Shu W, et al. A multigenic program mediating breast cancer
metastasis to bone. Cancer Cell 2003;3:537– 49.
9. Ree AH, Engebraaten O, Hovig E, Fodstad O. Differential display analysis of breast
carcinoma cells enriched by immunomagnetic target cell selection: gene expression
profiles in bone marrow target cells. Int J Cancer 2002;97:28 –33.
10. Wyckoff JB, Jones JG, Condeelis JS, Segall JE. A critical step in metastasis: in vivo
analysis of intravasation at the primary tumor. Cancer Res 2000;60:2504 –11.
11. Wyckoff JB, Segall JE, Condeelis JS. The collection of the motile population of cells
from a living tumor. Cancer Res 2000;60:5401– 4.
12. Lin EY, Gouon-Evans V, Nguyen AV, Pollard JW. The macrophage growth factor
CSF-1 in mammary gland development and tumor progression. J Mammary Gland
Biol Neoplasia 2002;7:147– 62.
13. Lin EY, Nguyen AV, Russell RG, Pollard JW. Colony-stimulating factor 1 promotes
progression of mammary tumors to malignancy. J Exp Med 2001;193:727– 40.
14. Wang W, Wyckoff JB, Wang Y, Bottinger EP, Segall JE, Condeelis JS. Gene
expression analysis on small numbers of invasive cells collected by chemotaxis from
primary mammary tumors of the mouse. BMC Biotechnol 2003;3:13.
15. Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD. Reverse transcriptase
template switching: a SMART approach for full-length cDNA library construction.
Biotechniques 2001;30:892–7.
16. Farina KL, Wyckoff JB, Rivera J, et al. Cell motility of tumor cells visualized in
living intact primary tumors using green fluorescent protein. Cancer Res 1998;58:
2528 –32.
17. Wang W, Wyckoff JB, Frohlich VC, et al. Single cell behavior in metastatic primary
mammary tumors correlated with gene expression patterns revealed by molecular
profiling. Cancer Res 2002;62:6278 88.
18. Goswami S, Sheets NL, Zavadil J, et al. Spectrum and range of oxidative stress
responses of human lens epithelial cells to H2O2 insult. Invest Ophthalmol Vis Sci
2003;44:2084 –93.
19. Novoradovskaya N, Whitfield ML, Basehore LS, et al. Universal Reference RNA as
a standard for microarray experiments. BMC Genomics 2004;5:20.
20. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the
ionizing radiation response. Proc Natl Acad Sci USA 2001;98:5116 –21.
21. Farina KL, Huttelmaier S, Musunuru K, Darnell R, Singer RH. Two ZBP1 KH
domains facilitate beta-actin mRNA localization, granule formation, and cytoskeletal
attachment. J Cell Biol 2003;160:77– 87.
22. Dal Canto RA, Shaw MK, Nolan GP, Steinman L, Fathman CG. Local delivery of
TNF by retrovirus-transduced T lymphocytes exacerbates experimental autoimmune
encephalomyelitis. Clin Immunol 1999;90:10 4.
23. Shestakova EA, Wyckoff J, Jones J, Singer RH, Condeelis J. Correlation of beta-actin
messenger RNA localization with metastatic potential in rat adenocarcinoma cell
lines. Cancer Res 1999;59:1202–5.
24. Segall JE, Tyerech S, Boselli L, et al. EGF stimulates lamellipod extension in
metastatic mammary adenocarcinoma cells by an actin-dependent mechanism. Clin
Exp Metastasis 1996;14:61–72.
25. Nicholson RI, Gee JM, Harper ME. EGFR and cancer prognosis. Eur J Cancer
2001;37 Suppl 4:S9 –15.
26. O’Sullivan C, Lewis CE, Harris AL, McGee JO. Secretion of epidermal growth factor
by macrophages associated with breast carcinoma. Lancet 1993;342:148 –9.
27. LeBedis C, Chen K, Fallavollita L, Boutros T, Brodt P. Peripheral lymph node
stromal cells can promote growth and tumorigenicity of breast carcinoma cells
through the release of IGF-I and EGF. Int J Cancer 2002;100:2– 8.
28. Condeelis J, Segall JE. Intravital imaging of cell movement in tumours. Nat Rev
Cancer 2003;3:921–30.
29. Mariadason JM, Arango D, Corner GA, et al. A gene expression profile that defines
colon cell maturation in vitro. Cancer Res 2002;62:4791– 804.
30. Bravo SB, Pampin S, Cameselle-Teijeiro J, et al. TGF-beta-induced apoptosis in
human thyrocytes is mediated by p27kip1 reduction and is overridden in neoplastic
thyrocytes by NF-kappaB activation. Oncogene 2003;22:7819 –30.
31. Goswami S, Wang W, Wyckoff JB, Condeelis JS. Breast cancer cells isolated by
chemotaxis from primary tumors show increased survival and resistance to chemo-
therapy. Cancer Res In press 2004.
32. Bailly M, Condeelis J. Cell motility: insights from the backstage. Nat Cell Biol
2002;4:E292– 4.
33. Mogilner A, Edelstein-Keshet L. Regulation of actin dynamics in rapidly moving
cells: a quantitative analysis. Biophys J 2002;83:1237–58.
34. Loisel TP, Boujemaa R, Pantaloni D, Carlier MF. Reconstitution of actin-based
motility of Listeria and Shigella using pure proteins. Nature (Lond) 1999;401:613– 6.
35. Ohashi K, Nagata K, Maekawa M, Ishizaki T, Narumiya S, Mizuno K. Rho-
associated kinase ROCK activates LIM-kinase 1 by phosphorylation at threonine 508
within the activation loop. J Biol Chem 2000;275:3577– 82.
36. Edwards DC, Sanders LC, Bokoch GM, Gill GN. Activation of LIM-kinase by Pak1
couples Rac/Cdc42 GTPase signalling to actin cytoskeletal dynamics. Nat Cell Biol
1999;1:253–9.
37. Cooper JA, Schafer DA. Control of actin assembly and disassembly at filament ends.
Curr Opin Cell Biol 2000;12:97–103.
38. Bear JE, Svitkina TM, Krause M, et al. Antagonism between Ena/VASP proteins and
actin filament capping regulates fibroblast motility. Cell 2002;109:509 –21.
39. Shestakova EA, Singer RH, Condeelis J. The physiological significance of beta-actin
mRNA localization in determining cell polarity and directional motility. Proc Natl
Acad Sci USA 2001;98:7045–50.
40. Evan GI, Vousden KH. Proliferation, cell cycle and apoptosis in cancer. Nature
(Lond) 2001;411:342– 8.
41. van ’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts
clinical outcome of breast cancer. Nature (Lond) 2002;415:530 6.
42. Bailly M, Ichetovkin I, Grant W, et al. The F-actin side binding activity of the Arp2/3
complex is essential for actin nucleation and lamellipod extension. Curr Biol 2001;
11:620 –5.
43. Chan AY, Bailly M, Zebda N, Segall JE, Condeelis JS. Role of cofilin in epidermal
growth factor-stimulated actin polymerization and lamellipod protrusion. J Cell Biol
2000;148:531– 42.
44. Ghosh M, Song X, Mouneimne G, Sidani M, Lawrence DS, Condeelis JS. Cofilin
promotes actin polymerization and defines the direction of cell motility. Science
(Wash DC) 2004;304:743– 6.
45. Mouneimne G, Soon L, DesMarais V, et al. Phospholipase C and cofilin are required
for carcinoma cell directionality in response to EGF stimulation. J Cell Biology
2004;166:697–708.
46. Devreotes P, Janetopoulos C. Eukaryotic chemotaxis: distinctions between directional
sensing and polarization. J Biol Chem 2003;278:20445– 8.
47. Carlier MF. Control of actin dynamics. Curr Opin Cell Biol 1998;10:45–51.
48. Davila M, Frost AR, Grizzle WE, Chakrabarti R. LIM kinase 1 is essential for the
invasive growth of prostate epithelial cells: implications in prostate cancer. J Biol
Chem 2003;278:36868 –75.
49. Yoshioka K, Foletta V, Bernard O, Itoh K. A role for LIM kinase in cancer invasion.
Proc Natl Acad Sci USA 2003;100:7247–52.
8593
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
50. Zebda N, Bernard O, Bailly M, Welti S, Lawrence DS, Condeelis JS. Phosphorylation
of ADF/cofilin abolishes EGF-induced actin nucleation at the leading edge and
subsequent lamellipod extension. J Cell Biol 2000;151:1119 –28.
51. Sahai E, Olson MF, Marshall CJ. Cross-talk between Ras and Rho signalling pathways in
transformation favours proliferation and increased motility. EMBO J 2001;20:755– 66.
52. Kamai T, Tsujii T, Arai K, et al. Significant association of Rho/ROCK pathway with
invasion and metastasis of bladder cancer. Clin Cancer Res 2003;9:2632– 41.
53. Di Modugno F, Bronzi G, Scanlan MJ, et al. Human Mena protein, a serex-defined
antigen overexpressed in breast cancer eliciting both humoral and CD8 T-cell
immune response. Int J Cancer 2004;109:909 –18.
54. Otsubo T, Iwaya K, Mukai Y, et al. Involvement of Arp2/3 complex in the process
of colorectal carcinogenesis. Mod Pathol 2004;17:461–7.
55. Parent CA, Devreotes PN. A cell’s sense of direction. Science (Wash DC) 1999;284:
765–70.
56. Iijima M, Huang YE, Devreotes P. Temporal and spatial regulation of chemotaxis.
Dev Cell 2002;3:469 –78.
57. Yaniv K, Yisraeli JK. The involvement of a conserved family of RNA binding
proteins in embryonic development and carcinogenesis. Gene 2002;287:49 –54.
58. Tessier CR, Doyle GA, Clark BA, Pitot HC, Ross J. Mammary tumor induction
in transgenic mice expressing an RNA-binding protein. Cancer Res 2004;64:
209 –14.
59. Herrick DJ, Ross J. The half-life of c-myc mRNA in growing and serum-stimulated
cells: influence of the coding and 3 untranslated regions and role of ribosome
translocation. Mol Cell Biol 1994;14:2119 –28.
8594
INVASIVE CARCINOMA CELLS WITHIN PRIMARY MAMMARY TUMORS
    • "It facilitates cell invasion via phosphatidylinositol 3-kinase- dependent local accumulation of actin filaments [32]. Increased ENAH/Mena expression levels correlate with invasiveness of breast and salivary gland tumors[33, 34], and are also seen in colorectal cancer and in polyps with high grade dysplasia [35]. TNS1 (up-regulated 14-fold), which is also known as Tensin 1, has actin cross-linking activity and localizes to focal adhesions. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Transgelin is an actin-binding protein that promotes motility in normal cells. Although the role of transgelin in cancer is controversial, a number of studies have shown that elevated levels correlate with aggressive tumor behavior, advanced stage, and poor prognosis. Here we sought to determine the role of transgelin more directly by determining whether experimental manipulation of transgelin levels in colorectal cancer (CRC) cells led to changes in metastatic potential in vivo. Methods Isogenic CRC cell lines that differ in transgelin expression were characterized using in vitro assays of growth and invasiveness and a mouse tail vein assay of experimental metastasis. Downstream effects of transgelin overexpression were investigated by gene expression profiling and quantitative PCR. Results Stable overexpression of transgelin in RKO cells, which have low endogenous levels, led to increased invasiveness, growth at low density, and growth in soft agar. Overexpression also led to an increase in the number and size of lung metastases in the mouse tail vein injection model. Similarly, attenuation of transgelin expression in HCT116 cells, which have high endogenous levels, decreased metastases in the same model. Investigation of mRNA expression patterns showed that transgelin overexpression altered the levels of approximately 250 other transcripts, with over-representation of genes that affect function of actin or other cytoskeletal proteins. Changes included increases in HOOK1, SDCCAG8, ENAH/Mena, and TNS1 and decreases in EMB, BCL11B, and PTPRD. Conclusions Increases or decreases in transgelin levels have reciprocal effects on tumor cell behavior, with higher expression promoting metastasis. Chronic overexpression influences steady-state levels of mRNAs for metastasis-related genes.
    Full-text · Article · Dec 2016
    • "It has been shown that interfering with LIMK function either by using RNAi (16) or by overexpression of a dominant negative form of LIMK1 in metastatic breast cancer cells (34), or by pharmacological LIMK inhibition (41) results in reduced cell invasiveness. Moreover, as LIMK expression is correlated with the aggressiveness of cancer cells (13,19,20) we decided to investigate if LIMK inhibition by Pyr1 impacted TS/A-pGL3, MDA-MB-231 and MDA-MB-231-ZNF217rvLuc2 invasiveness in vitro. "
    [Show abstract] [Hide abstract] ABSTRACT: LIM kinases (LIMK) are emerging targets for cancer therapy and they function as network hubs to coordinate actin and microtubule dynamics. When LIMK are inhibited, actin microfilaments are disorganized and microtubules are stabilized. Owing to their stabilizing effect on microtubules, LIMK inhibitors may provide an therapeutic strategy to treat taxane-resistant cancers. In this study, we investigated the effect of LIMK inhibition on breast tumor development and on paclitaxel resistant tumors, using a novel selective LIMK inhibitor termed Pyr1. Treatment of breast cancer cells, including paclitaxel-resistant cells, blocked their invasion and proliferation in vitro and their growth in vivo in tumor xenograft assays. The tumor invasive properties of Pyr1 were investigated in vivo by intravital microscopy of tumor xenografts. A striking change in cell morphology was observed with a rounded phenotype arising in a subpopulation of cells while other cells remained elongated. Notably, although Pyr1 decreased the motility of elongated cells it increased the motility of rounded cells in the tumor. Pyr1 administration prevented the growth of metastasis but not their spread. Overall, our results provided a preclinical proof of concept concerning how a small molecule inhibitor of LIMK kinases may offer a strategy to treat taxane-resistant breast tumors and metastases.
    Full-text · Article · May 2016
    • "Mena is an actin-binding protein that is involved in the regulation of cofilin-stimulated actin polymerization, the key cofilin activity that determines chemotactic direction and invasion (Gertler and Condeelis, 2011; Philippar et al., 2008; Roussos et al., 2011a,b,c) (see poster). Subsequent investigation of the HIS gene expression pathways has revealed that differentially spliced Mena isoforms, including Mena INV , are upregulated, whereas the invasionsuppressing Mena11a isoform is downregulated in the migratory– disseminating tumor cell subpopulation (Goswami et al., 2009; Patsialou et al., 2013 Patsialou et al., , 2012 Roussos et al., 2011a,b,c; Wang et al., 2004). This isoform-splicing pattern of Mena (Mena INV-high and Mena11a low ) is functionally crucial for tumor cell invasion and migration during the metastatic cascade. "
    [Show abstract] [Hide abstract] ABSTRACT: Gene expression profiling has yielded expression signatures from which prognostic tests can be derived to facilitate clinical decision making in breast cancer patients. Some of these signatures are based on profiling of whole tumor tissue (tissue signatures), which includes all tumor and stromal cells. Prognostic markers have also been derived from the profiling of metastasizing tumor cells, including circulating tumor cells (CTCs) and migratory-disseminating tumor cells within the primary tumor. The metastasis signatures based on CTCs and migratory-disseminating tumor cells have greater potential for unraveling cell biology insights and mechanistic underpinnings of tumor cell dissemination and metastasis. Of clinical interest is the promise that stratification of patients into high or low metastatic risk, as well as assessing the need for cytotoxic therapy, might be improved if prognostics derived from these two types of signatures are used in a combined way. The aim of this Cell Science at a Glance article and accompanying poster is to navigate through both types of signatures and their derived prognostics, as well as to highlight biological insights and clinical applications that could be derived from them, especially when they are used in combination.
    Full-text · Article · Apr 2016
Show more