| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
Molecular Biology and Genetics |
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115 [K. J. M., B. M. K., L. M. P., B. K., C-P. K., X. Z., A. B. P., R. S.]; Ludwig Institute for Cancer Research/University College London, Breast Cancer Laboratory, University College London Medical School, London W1P 6DB, United Kingdom [A. M., M. J. O.]; and Departments of Surgery [C. M. K.] and Pathology [G. L. M.], Brigham and Women’s Hospital, Boston, Massachusetts 02115
 |
ABSTRACT |
|---|
 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
|
INTRODUCTION |
|---|
|
TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
In cancer prognosis and treatment, a shortcoming currently is the lack of methods that adequately address the complexity and diversity of the disease. Cancer is a highly heterogeneous disease, both morphologically and genetically (5) . No simple relationship has been demonstrated between a mutation or the expression level of a given gene or protein and a certain etiology or extent of disease (6) . Prognostic marker systems based on single parameters have generally proven inadequate. Thus, multiparametric methods, methods that rely on many pieces of information, are ideally suited to the grouping of tumor subtypes.
mRNA fingerprints that represent the expression patterns of large numbers of genes have the potential to allow precise and accurate grouping of tumor subtypes. cDNA microarrays recently were used to compare the changes in expression patterns of a set of 5000 randomly selected genes in breast tumor tissue and cultured breast epithelial cells (7) . The results demonstrated two types of expression changes: (a) those relevant to the tumorigenic process of breast epithelial cells; and (b) those irrelevant to the disease and, in some cases, originating from nontumor cells that were present in varying amounts in the tumor biopsies. As a first step toward identifying physiologically relevant gene expression changes, clusters of genes that responded in a concerted manner to exogenously added growth factors were identified (7) .
We report here a different approach to identify clinically relevant changes in gene expression. DD4 reverse transcription-PCR (8 , 9) was used as a primary screen to identify a set of 170 genes that were expressed by breast epithelial cells and were differentially expressed in a breast cancer cell line. DD comparisons were made between sorted normal breast epithelial cells (10) and a highly malignant breast tumor cell line, MDA-MB-435 (11) . High-density, membrane-based hybridization arrays were then developed to assay expression patterns in cultured breast cells and biopsied tumor tissues. Using cluster analysis (12) , a computer algorithm developed to analyze cDNA microarray data, groups of genes with expression patterns that correlated with clinical parameters were identified.
Four clusters of genes are reported here, the expression of which was associated with three major parameters used clinically to characterize breast tumors: ER status, tumor stage, and tumor size. Expression patterns of these clusters were then used to group breast cancer patients into distinct categories. The feasibility of building a large database of expression patterns linked to clinical information is demonstrated by these results. The four gene clusters described represent an initial step in the process of identifying useful sets of marker genes and will likely have their greatest utility when used in combination with additional sets of physiologically relevant genes.
|
MATERIALS AND METHODS |
|---|
|
TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
DD Reverse Transcription-PCR.
DD was performed as described (9)
to compare normal breast
epithelial cells and a metastatic breast tumor cell line, MDA-MB-435
(11)
. The normal cells used were either 76N cultured
breast epithelial cells (14)
or sorted normal breast
myoepithelial and luminal epithelial cells (10)
. Both up-
and down-regulated cDNA bands were selected for analysis. Approximately
70 primer pairs were used, including: (a) LHA-1, -2, -3, -4,
-5, -6, and -7 in combination with LHT11-G, -A,
and -C (9)
; (b) E1-OPA-1, -2, -3, -7, and –8
and H3-OPA-4, -5, -6, -9, and -10 in combination with
LHT11-G, -A, -C (9)
; (c)
ARP-1, -2, -3, and -4 in combination with AP-1, -2, -3, -4, -5, -6, -7,
-8, -9, and -10 (Genomyx/Beckman Corp., Foster City, CA); and
(d) AP-1, -2, -3, -4, and -5 in combination with T12 M-G,
-A, -T, and -C (GenHunter Corp., Nashville, TN). PCR conditions were as
recommended by the respective primer kit manufacturers or as described
(9)
for the LHA and OPA series of primers. Gel
electrophoresis was performed on the extended format of the
programmable Genomyx LR apparatus (Genomyx/Beckman Corp.). DD bands
were eluted and precipitated (16)
. Either of three
different approaches was then used to identify the bands and obtain a
cDNA clone: (a) cDNAs were TA-cloned into pCR2 or pCR2.1
(Invitrogen, Carlesbad, CA) directly and colonies were screened for
differential expressors, which were then sequenced; (b)
cDNAs were directly sequenced (16
, 17)
, a gene-specific
primer was made, and the PCR product was then TA-cloned and sequenced
to confirm cloning of the correct cDNA; or (c) cDNAs were
directly sequenced, TA-cloned, and sequenced to confirm cloning of the
correct cDNA. Genes were identified by querying GenBank using the BLAST
algorithm (18)
, as described (16)
.
Hybridization Arrays.
Membrane arrays with tags for 124 different genes were made by spotting
PCR products or whole plasmids using a hand-held 96-pin-spotting
device. The templates for PCR were DD-isolated cDNA fragments that were
either cloned into the pCR2.1 TA-cloning vector (Invitrogen) or were
used directly following DD gel-elution without cloning. Cloned tags
were amplified using M13 vector primers. Uncloned tags were amplified
using one DD primer in combination with one gene-specific primer
(16
, 17)
. After PCR, samples were purified on QIAquick PCR
purification spin columns (Qiagen Inc., Valencia, CA). An aloquot of
each was electrophoresed on an agarose gel, and products were
quantitated and size-checked by comparison to standards. In a few
cases, whole plasmids consisting of cDNA inserted into the pCR2.1
TA-cloning vector (Invitrogen) or the vector alone were prepared using
a mini-prep kit (Qiagen Inc.), quantitated by electrophoresis, and
arrayed directly. cDNA-tags were spotted onto positively charged nylon
membranes (Micron Separations Inc., Westboro, MA) using a multiprint
96-pin replicator with 16 offset positions (V&P Scientific, Inc., San
Diego, CA) to give 1536 spots per 3.5 x 5 inch
membrane. Each tag at a concentration of 
1 µg/µl was incubated
10 min in 0.4 M NaOH, 10 mM EDTA to denature, then chilled
on ice. An aliquot was diluted 1:30, and the tags were applied in
quadruplicate at both concentrations, the stock 1 µg/µl and the
diluted 0.03 µg/µl. The spotting device delivered 0.1 µl (as
per manufacturer) with high reproducibility. In a control experiment
where three different 32P-labeled gene tags were
applied to a membrane, which was then UV cross-linked, rinsed, and
quantitated by phosphorimaging, the SE was 4–8% of the mean for 12
sets of 16 spots (data not shown). Twenty replicate membranes with the
124 different gene tags were prepared in parallel, with thorough
cleaning of the replicator pins between every three membranes. cDNA
tags were UV cross-linked to the membranes, then stored in sealed bags
at 4°C.
Radiolabeled cDNA probes were prepared from 5 µg of total cellular
RNA by incorporating 50 µCi 
32P-dCTP into
first-strand cDNA, as described (16)
. Incorporation rates
of 5–20% were standard. Probes with incorporation rates of <3% were
not used. Membranes were prehybridized 3 h in a formamide-based
hybridization buffer (ExpressHyb; Clonetech Corp., Palo Alto, CA) at
68°C. The entire radiolabeled cDNA probe was then added to the
buffer, and membranes were hybridized 14–18 h at 68°C. Membranes
were washed, as recommended by the manufacturer of the hybridization
solution, and exposed to a phosphor-imaging screen for 2 days. After
scanning, membranes were stripped and used again for a total of four
hybridizations. MCF-10A and 21PT profiles were averaged from three
repeated experiments each, whereas myoepithelial, luminal epithelial,
MDA-MB-435, and PT-4 profiles were averaged from two repeated
experiments each. Other profiles represent individual experiments.
Data Analysis.
To quantify signal intensities of the hybridized spots, equal-sized
ellipses were drawn around all spots using software (ImageQuant)
provided with the phosphorimager (Molecular Dynamics, Sunnyvale, CA).
Data from only the higher concentration spots were used. Median
background was subtracted, and signals that were <5-fold above
background level were considered too low to accurately measure
(background). Mean signals were calculated from quadruplicate
measurable spots, or if three of the four spots were measurable. Sets
with SEs exceeding 150% of the mean were disregarded (not
detected). Signal intensities for each membrane were normalized
to the median signal of that membrane. For RNAs run multiple times
geometric means of all non-BKG membrane-normalized values were
calculated. A single median BKG value was determined from an entire set
of membranes being compared, and this value was substituted for all BKG
values. Signals for each individual gene were then normalized to the
geometric mean of the expression level of that gene across the set of
membranes being compared. Genes with consistently low signals across an
entire set of comparison membranes were omitted from the analysis.
Cluster analysis was performed using publicly available software written by M. Eisen (Stanford University, Stanford, CA).5 Data sets were logarithmically transformed, and the similarity metric was an uncentered correlation. An image contrast of 3 or 4 was used. Raw data tables, full cluster diagrams with gene identities, and other materials are available.6
|
RESULTS |
|---|
|
TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
7,000 genes, which, because
cells express 15,000 mRNAs (19)
, represents over one-third
of all expressed genes. Four and one-half percent of all mRNAs were
differential; hence, a total of 700 genes were differentially expressed
between the cancer and normal cells. This number is in agreement with
other studies using different methods (20)
. A set of 170
genes that were differentially expressed was identified (a complete
list of known genes with names and GenBank accession numbers is
available).6
These 170 genes represent
approximately one-quarter of all of the genes that were differentially
expressed in the normal and cancer cells compared, whereas the 107
genes included on the hybridization arrays represent one-seventh.
The great majority of the differential genes we observed were similarly
expressed in all of the normal breast cell types, but were either down-
or up-regulated in the tumor cells. A marked contrast was seen in the
types of genes that comprised the down- and up-regulated categories.
Nearly 70% of the genes that were down-regulated in the tumor cells
were categorized as filamentous, cell surface, and secreted genes that
play roles in adhesion, communication, and the maintenance of cell
shape (Table 1)
. In contrast, 75% of the known genes, the expression of which was
up-regulated in tumor cells, were enzymes involved in metabolism,
macromolecular synthesis, and disruption of the extracellular matrix.
|
. A full listing of the genes included on the arrays is
available.6
|
. In five experiments
using MCF-10A RNA, 95% of values deviated by <5-fold (data not
shown). Based on the level of reproducibility of this set of array
experiments, individual gene expression changes of <5-fold were not
considered significant. In addition, no conclusions based on a single
data point have been made. The array assay, as performed, was highly
sensitive for a non-PCR-based assay, as indicated by the dynamic range,
which covered nearly four orders of magnitude (Fig. 1B)
.
Furthermore, expression signals from >90% of the DD genes could be
measured, which was considerably better than our detection rate of
60% (14 of 22) with conventional Northern assays for the same set
of DD genes (Ref. 17
and data not shown).
Identification of Gene Clusters with Clinically Relevant Expression
Patterns.
mRNA expression levels of 124 genes were assayed using
hybridization arrays in breast tumor tissue samples obtained from 18
patients and seven breast cell lines. Color-coded relative expression
levels are displayed in Fig. 2
. Data from individual genes are shown in rows and tissues in columns.
Cluster analysis was used to organize the genes and the tissues so that
those with the most closely related expression patterns are positioned
adjacent to each other (12)
. Cluster analysis is a
computer method that calculates correlation coefficients between all
gene and tissue data sets. It then organizes the positions of the rows
and columns of the display and generates hierarchical trees that
indicate the degree of relatedness by the height of nodes.
|
. The group at the right
included predominantly ER-positive samples, whereas the group at the
left included predominantly ER-negative samples. This
division indicated that the arrays included many genes with expression
patterns that reflected ER status.
Seven well characterized breast cell lines are included with the tumor
tissues on Fig. 2
. The manner in which cluster analysis organized these
cell lines demonstrated its ability to accurately recognize and make
groupings according to physiologically relevant characteristics. The
21T series of cell lines (21 MT-1, 21 MT-2, 21NT, and 21PT), which were
all derived from a single breast cancer patient (13)
, were
grouped together in a closely related cluster at the left.
21PT cells, which are unable to form tumors in nude mice
(13)
, were positioned on a deeper node reflecting a more
distant relationship to the three other 21T cell lines, which are all
tumorigenic in mice (13)
. The highly metastatic breast
tumor cell line MDA-MB-435 (MDA-435) (11)
was found to be
more similar to the 21T series of tumor cell lines than was the
immortal but nonmalignant breast cell line MCF-10A (21)
.
The single ER-positive cell line, MCF7 (22)
, was widely
separated from the six ER-negative cell lines.
To identify genes with clinically relevant expression patterns, charts with tissues ordered by various clinical parameters were prepared. These were then screened for individual genes and gene clusters with expression patterns that increased or decreased across the chart. The significance of resulting Ps (Fisher’s exact test) was considered in the context of the multigene and multitest analysis. Because we assayed 124 genes, a P for an individual gene must be <1–0.95(1/124) or <0.0004 to be considered statistically significant, if we had performed a single test. We tested for the association of gene expression with six parameters: ER status, tumor stage, grade, size, the percentage of S phase cells, and patient age. Hence, statistically meaningful Ps for individual genes must be <1–0.95(6/124) or <0.00007. Failure to meet this level does not mean that the gene is not a good marker, but rather that the current experimental design did not prove that it is.
Seven individual genes were found to have P < 0.0004, which suggested a possible association with a clinical parameter. All seven were associated with ER status. They included keratin 19 (P < 0.0001 and 0.0002), PAG (P = 0.0002), unknown 94 (P = 0.0001), PDAC-2 (P = 0.0002), unknown 102f (P = 0.0002), and lysosomal sialyltransferase (P = 0.0001). No other individual genes were found to be significantly associated with any other clinical parameter.
In a multigene and multitest study it may be more appropriate to
address overall patterns of results (e.g., clustering). Gene
clusters with mean expression levels that showed important associations
with clinical parameters are shown (Fig. 2)
. Two clusters were
associated with ER status, one with clinical stage, and one with tumor
size. No clusters were associated with tumor grade, percentage of S
phase cells, or patient age. Ps (Fisher’s exact test)
calculated using the average gene expression data shown in the
top row of each cluster are shown in an attempt to summarize
and compare the significance levels of the four clusters. Because of
the complexity of the analysis, these Ps are not
statistically interpretable.
ER status is a major clinical grouping in breast cancer that is
routinely measured to predict responsiveness to antihormone therapy.
Clusters I and II were strongly ER-associated and expressed inversely
to each other (r = -0.50,
P = 0.012). Identities of genes in these
clusters are shown (Fig. 2)
. Expression of the p53 cluster (cluster I)
was higher in ER-positive tissues than ER-negative tissues. Expression
of the maspin cluster (cluster II) was the inverse.
A second major clinical grouping applied to breast cancer is tumor stage, which takes into account information on tumor size, nodal status, and distant metastases (23) . Gene cluster III, which included HSP-90, tended to be overexpressed in stage IV tumors relative to stages I, II, and III tumors. Stage IV breast tumors are distinguished from earlier stage tumors by the presence of distant metastases. Clinical stage is currently the best indicator of disease prognosis (23) , and, hence, this cluster may represent a valuable set of markers that provide prognostic information.
Tumor size is also an important independent predictor of disease prognosis (23) . Gene cluster IV, which included keratin 14, was reduced in expression in tumors larger than 1.5 cm relative to smaller tumors.
Using the Clinically Relevant Gene Clusters to Categorize Breast
Tumors.
We have used cluster analysis and the expression patterns of the four
clinically relevant gene clusters to group breast tumor tissue into
categories. Results are shown in Fig. 3
. This analysis sorted the tumors into two major groups that differed in
their ER status (P = 0.0002). The ER-negative
group is the top, gray group in Fig. 3
. Grouping by other
clinical parameters is also apparent. For example, two highly related
groups of tumors (group 1: H16, H4, and H43, group 2: PT-10 and PT-6)
included tumors with similar clinical data. The former group was
advanced stage, ER-negative tumors, whereas the latter group was
ER-positive, stage II invasive carcinomas from women of similar ages.
|
|
DISCUSSION |
|---|
|
TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
We describe here an approach that used a DD prescreening step and allowed us to identify a number of genes with physiologically relevant expression patterns. The DD step increased the probability that the arrayed tags detected genes that were expressed by breast epithelial cells and were differential in breast cancer. Other methods of preselecting tissue-appropriate genes for inclusion on arrays have been reported (25) . These have also reported markedly better rates of differential expression in cancer cells than array methods using random gene collections. Recent studies have described the results of cDNA microarray hybridizations and cluster analysis of breast and colon tumor tissues (7 , 26) . These studies did not report the clustering of tissues based on ER status or clinical stage. Both of these studies used large unselected collections of arrayed gene tags that represented genes expressed in a wide variety of cell types, including fibroblasts, stromal cells, adipocytes, and epithelial cells. Whereas we point out that our current study was not an exhaustive analysis and that many important genes were certainly missed by limiting the comparison to a single tumor cell line, our results nevertheless demonstrate the usefulness of the DD preselection approach and identify several potentially useful marker genes.
The types of genes identified here by DD gives information on the process of tumorigenesis. The tumor cells generally lost the expression of genes involved in cell adhesion, communication, and the maintenance of cell shape, whereas they generally gained the expression of synthetic and metabolic enzymes important for cell proliferation. These general processes are well studied as important events in tumorigenesis.
A large number of the genes isolated by DD were associated with
ER status. Of the 107 DD-identified genes tested here, 31 (or 29%)
were included in clusters with expression levels that were
significantly correlated with ER status. Twenty-two genes were
expressed at high levels in ER-positive tumors, whereas nine genes were
expressed at low levels in ER-positive tumors. The finding of many ER
status-associated genes was surprising based on literature reports that
generally describe remarkably few direct target genes of the ER. These
include the ER gene itself, the progesterone receptor, pS2, AR, LIV1,
keratin 19 (27)
, 1-antichymotrypsin (28)
,
complement component 3 (29)
, and HSP-27 (30)
.
In explanation, the genes we found likely include directly and
indirectly regulated genes, whereas genes previously identified include
only directly regulated genes. The direct regulation by ER of only a
few prolific factors could result in many indirect changes in gene
expression.
Several of the genes identified here as ER status-associated were
previously reported to be ER-regulated or differentially expressed as a
function of ER status. Keratin 19 gene expression and filament
organization are regulated by estrogen (27)
. Keratin 19
mRNA was identified as one of 10 highly overexpressed mRNAs in a cDNA
microarray comparison of two ER-positive breast tumor cell lines
relative to two ER-negative lines (31)
. Studies of p53
have generally addressed protein expression levels, and very few
studies have addressed p53 mRNA levels. A tendency toward the
association of p53 mRNA expression levels with ER status was noted in
one study (32)
, although it is well known that p53 protein
levels are not associated with ER status (see, for example, Ref.
33
). CD59, a cell surface component of the complement
system, protects cells from complement attack. Protein levels of CD59
are high in the ER-positive breast tumor cell line MCF7
(34)
. Other complement component genes are known to be
estrogen responsive (35)
.1-Antichymotrypsin was the
first estradiol-induced protein to be identified (28)
,
although its levels of expression in ER-positive versus
ER-negative cell lines or tissues have not been reported. Likewise, CC3
is also an estrogen-responsive gene (29)
, the mRNA
expression levels of which have not been studied in tumor tissue.
Histone H4 is a commonly used proliferation marker, and proliferation
rates are generally reduced in ER-positive relative to ER-negative
breast tumors (36)
. Maspin and elafin are both protease
inhibitors that are down-regulated in breast cancer cells (37
, 38)
. Neither has been previously studied in relation to ER
status.
It is reasonable to think that ER-positive and ER-negative cells represent two very different tumor cell types in breast cancer. Not only do our results indicate that ER status is associated with widespread changes in gene expression patterns in tumor cells, but ER status is commonly used as a major clinical grouping in breast cancer. One can postulate that cancer cells with ER-positive gene expression patterns arise either from specialized cells that naturally require estrogen for their growth or by aberrantly using the hormone system to facilitate their growth. Cancer cells with ER-negative gene expression patterns, on the other hand, may include tumors that arise from nonhormone-responsive breast cells through mechanisms commonly used by tumor cells in nonhormonal tissues.
Useful clinical information may be provided by the expression patterns of ER status-associated genes in individual tumors. In particular, these patterns may help to identify tamoxifen nonresponsive patients. Patients with ER-positive tumors, as assayed clinically by standard ligand-binding or immunostaining assays, are typically treated with the antiestrogen tamoxifen. Response rates of up to 60% have been reported (39) . Nonresponders may include patients with tumors that express an ER that can bind to ligands but does not function as a transcription activator. The expression patterns may show if a functional ER is present. In our study, one clinically ER-positive tumor (H16) was grouped with ER-tumors. This tumor may express a receptor capable of binding to estrogen but otherwise nonfunctional and, hence, unable to activate downstream genes. Such a tumor would be predicted to be unresponsive to treatment with antiestrogens.
A recent study has described four subclasses of ER status based on the presence and functionality of the receptor (24) . These include: (a) fully normal ER that binds to its ligands and exhibits ligand-dependent DNA-binding and transcription activation; (b) ER that bind to ligands but is not functional; (c) ER that does not bind to ligands, but constitutively binds to DNA and activates transcription; and (d) cells devoid of ER. The first three subtypes would be categorized as ER positive by clinical immunoassays, although the latter three subtypes may not respond to antiestrogen therapy. Assays that assess receptor function, such as the DNA-binding assay described previously (24) or the ER status-associated expression patterns described here, may help to identify the 40% of ER-positive breast cancer patients (39) who do not respond to tamoxifen.
The clinical stage-associated cluster identified here included HSP-90. Literature evidence supports a role for this gene in advanced breast tumors. HSP-90 was elevated in breast tumor tissue, and antibodies to HSP-90 were associated with poor survival in breast cancer (40 , 41) . Furthermore, HSP-90 plays a role in ER signal transduction, apparently by increasing ER transcriptional activity in conditions of low estrogen (42) .
The tumor size-associated cluster included keratin 14, CD44, keratin 5,
and glutathione S-transferase 
. Keratin 14 is expressed specifically
by normal breast myoepithelial cells and not by breast carcinoma cells,
which typically express markers of luminal epithelial cells
(43)
. Markers for myoepithelial cells are present in
normal tissue, benign lesions, and ductal carcinoma in situ,
but are absent from invasive tumors (44
, 45)
. Keratin 14
loss in larger tumors likely reflects a reduced proportion of normal
cells and ductal carcinoma in situ in biopsy specimens from
larger tumors. The other genes in this cluster are also expressed
preferentially by normal myoepithelial cells (Ref. 46
and
data not shown).
We have used undissected tissue biopsies because this material is readily available for clinical analysis. Tissue biopsies, however, are composed of a variety of different cell types. The presence of nontumor cell types may allow us to gain contextual information on the tumor. It is important to consider, however, that the expression patterns reported did not necessarily originate from the tumor cells themselves. For example, maspin and elafin were found to be highly expressed in a number of advanced stage, ER-negative tumors. These two genes are generally down-regulated in metastatic breast tumor cell lines relative to normal breast myoepithelial and luminal epithelial cells (37 , 38) . Furthermore, maspin acts as a tumor suppressor in breast cancer (37) . These and other genes may be increased in expression in normal cells that are adjacent to tumor cells as a part of a natural tumor defense system (47) .
Detailed molecular characterization or fingerprinting methods are versatile in their potential to improve therapeutic decisions in many diseases, not only cancer. In the cancer field, these methods are applicable to any malignancy for which malignant cells are available for analysis. Newer methods are being developed to use very small samples of tissue. It is also feasible to begin to look at disseminated tumor cells circulating in the blood. A general approach for the development fingerprinting methods is to assemble a sufficient collection of marker genes relevant to a particular disease. Using these markers, a sizable database of expression patterns is then built from sources for which clinical histories are available. This database allows an assessment of the use of the method and also provides the backbone of information against which to compare each incoming sample for comparative prognostic and predictive information.
|
ACKNOWLEDGMENTS |
|---|
|
FOOTNOTES |
|---|
1 Funded by a grant from the Ludwig Institute for
Cancer Research. 
2 To whom requests for reprints should be
addressed, at Dana-Farber Cancer Institute, Department of Cancer
Biology, Cancer Institute, D610B, 44 Binney Street, Boston, MA 02115.
4 The abbreviations used are: DD, differential
display; ER, estrogen receptor.
5 http://rana.stanford.edu/clustering.
6 http://mbcf.dfci.harvard.edu/labs/pardee/expression_patterns.html.
Received 10/15/99. Accepted 2/16/00.
|
REFERENCES |
|---|
|
TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
1-antichymotrypsin in MCF7 and T47D human breast cancer cell lines. Mol. Cell. Endocrinol., 42: 207-214, 1985.[Medline]
This article has been cited by other articles:
|
|
 |
|
  V. Ramachandran, T. Arumugam, H. Wang, and C. D. Logsdon Anterior Gradient 2 Is Expressed and Secreted during the Development of Pancreatic Cancer and Promotes Cancer Cell Survival Cancer Res., October 1, 2008; 68(19): 7811 - 7818. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Lacroix Significance, detection and markers of disseminated breast cancer cells Endocr. Relat. Cancer, December 1, 2006; 13(4): 1033 - 1067. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Dolled-Filhart, L. Ryden, M. Cregger, K. Jirstrom, M. Harigopal, R. L. Camp, and D. L. Rimm Classification of breast cancer using genetic algorithms and tissue microarrays. Clin. Cancer Res., November 1, 2006; 12(21): 6459 - 6468. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Bertucci, D. Birnbaum, and A. Goncalves Proteomics of Breast Cancer: Principles and Potential Clinical Applications Mol. Cell. Proteomics, October 1, 2006; 5(10): 1772 - 1786. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Ostapkowicz, K. Inai, L. Smith, S. Kreda, and J. Spychala Lipid rafts remodeling in estrogen receptor-negative breast cancer is reversed by histone deacetylase inhibitor. Mol. Cancer Ther., February 1, 2006; 5(2): 238 - 245. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Liu, P. S. Rudland, D. R. Sibson, A. Platt-Higgins, and R. Barraclough Human Homologue of Cement Gland Protein, a Novel Metastasis Inducer Associated with Breast Carcinomas Cancer Res., May 1, 2005; 65(9): 3796 - 3805. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 X. Tian, M. R. Aruva, W. Qin, W. Zhu, K. T. Duffy, E. R. Sauter, M. L. Thakur, and E. Wickstrom External Imaging of CCND1 Cancer Gene Activity in Experimental Human Breast Cancer Xenografts with 99mTc-Peptide-Peptide Nucleic Acid-Peptide Chimeras J. Nucl. Med., December 1, 2004; 45(12): 2070 - 2082. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
O. Modlich, H.-B. Prisack, M. Munnes, W. Audretsch, and H. Bojar Immediate Gene Expression Changes After the First Course of Neoadjuvant Chemotherapy in Patients with Primary Breast Cancer Disease Clin. Cancer Res., October 1, 2004; 10(19): 6418 - 6431. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. A. Makretsov, D. G. Huntsman, T. O. Nielsen, E. Yorida, M. Peacock, M. C. U. Cheang, S. E. Dunn, M. Hayes, M. van de Rijn, C. Bajdik, et al. Hierarchical Clustering Analysis of Tissue Microarray Immunostaining Data Identifies Prognostically Significant Groups of Breast Carcinoma Clin. Cancer Res., September 15, 2004; 10(18): 6143 - 6151. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Yu, C. H. Lee, P. H. Tan, and P. Tan Conservation of Breast Cancer Molecular Subtypes and Transcriptional Patterns of Tumor Progression Across Distinct Ethnic Populations Clin. Cancer Res., August 15, 2004; 10(16): 5508 - 5517. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 H. Xie, Y. H. Yu, F. Xie, Y. Z. Lao, and Z. Gao Breast Cancer Susceptibility Gene mRNAs Quantified by Microarrays with Electrochemical Detection Clin. Chem., July 1, 2004; 50(7): 1231 - 1233. [Full Text] [PDF]
|
|
|
|
|
|
K. Yu, C. H. Lee, P. H. Tan, G. S. Hong, S. B. Wee, C. Y. Wong, and P. Tan A Molecular Signature of the Nottingham Prognostic Index in Breast Cancer Cancer Res., May 1, 2004; 64(9): 2962 - 2968. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. M. Selaru, J. Yin, A. Olaru, Y. Mori, Y. Xu, S. H. Epstein, F. Sato, E. Deacu, S. Wang, A. Sterian, et al. An Unsupervised Approach to Identify Molecular Phenotypic Components Influencing Breast Cancer Features Cancer Res., March 1, 2004; 64(5): 1584 - 1588. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. K. Gruvberger-Saal, P. Eden, M. Ringner, B. Baldetorp, G. Chebil, A. Borg, M. Ferno, C. Peterson, and P. S. Meltzer Predicting continuous values of prognostic markers in breast cancer from microarray gene expression profiles Mol. Cancer Ther., February 1, 2004; 3(2): 161 - 168. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Spychala, E. Lazarowski, A. Ostapkowicz, L. H. Ayscue, A. Jin, and B. S. Mitchell Role of Estrogen Receptor in the Regulation of Ecto-5'-Nucleotidase and Adenosine in Breast Cancer Clin. |
