Note: Descriptions are shown in the official language in which they were submitted.
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GENE EXPRESSION METHODS FOR SCREENING COMPOUNDS
The present application is a continuation-in-part application of U.S. Patent
Application No. 09/013,496, filed January 26, 1998, the disclosure of which is
incorporated herein by reference in its entirety for all purposes.
BACKGROUND
Differences in the expression of genes in normal versus activated,
diseased, neoplastic cells or the like can be helpful in understanding
cellular processes
resulting in the affected state. For example, Zhang et al. Science 276:1268-
1272
( 1997)) disclosed gene expression patterns in gastrointestinal tumors,
identifying more
than 500 transcripts that were expressed at significantly different levels in
normal and
neoplastic cells. Bernard et al. (Nucl. Acids Res. 24:1435-1442 (1996))
disclosed a
method for analyzing the expression levels of 47 genes in resting and
activated T cells,
as well as in epithelial cells.
Microarrays of synthetic oligonucleotides or cDNAs are useful in
evaluating differential gene expression. For example, Schena et al. Science
270: 467-
470 ( 1995) disclosed the quantitative monitoring of gene expression patterns
in response
to transgenes using a complementary DNA microarray. Shena et al. (Proc. Natl.
Acad.
Sci. U.S.A. 93(20):10614-10619 (1996)) used microassays containing human cDNAs
of
unknown sequence to quantitatively monitor differential gene expression
patterns under
given experimental conditions. De Risi et al. (Nat. Genet. 14(4):457-
460(1996)) used a
cDNA microarray to analyze gene expression patterns in human cancer. Heller et
al.
(Proc. Natl. Acad. Sci. U.S.A. 94(6):2150-2155 (1997)) disclosed the use of
cDNA
microarray technology to monitor gene expression in inflammation.
Other methods for screening include a method for detecting and isolating
differentially expressed mRNAs using first oligonucleotide primers for reverse
transcription of mRNAs and both the first oligonucleotide primers and second
oligonucleotide primers for amplification of the resultant cDNAs (U.S.
5,580,726).
Rosenberg et al. (PCT Publication WO 95/21944) disclosed the use of expressed
sequence tags (EST's) to detect genes differentially expressed in healthy
subjects vs.
subjects having a disease of interest. Lee et al. (Cell Biolo~y 92:8303-8307
(1995))
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disclosed the use of comparative expressed -sequence -tag analysis to identify
about 600
differentially expressed in RNAs in untreated and nerve growth factor-treated
PC12 cells.
Further screening methods include such examples as that of Nilsson et al.
(PCT Publication WO 93/07290) who disclosed an in vitro method of evaluating
the
antagonistic vs agonistic effects of a receptor-binding substance on selected
types of cells
containing endogenous intracellular hormone receptors by analyzing cellular
response to
the receptor-binding substance based on the level of expression of the protein
product
made by a gene regulated by the hormone-receptor interaction. WO 96/41013
disclosed
a method for identifying a receptor agonist or antagonist using mutant
versions of
intracellular receptors such as the estrogen (ER), androgen (AR), progesterone
(PR), and
glucocorticoid (GR) receptors.
Knowledge that environmental agents alter gene expression has led to the
employment of specific genes as biomarkers of exposure to chemicals and other
environmental factors (Links et al. (Annu. Rev. Public Health 16:83-103
(1995)). Such
biomarkers have been used to screen chemicals and biological samples for
ability to alter
gene expression (Sewall et al. Clin. Chem. 41:1829-1834 (1995)).
Thus, a need exists for methods to screen and characterize differential
gene expression in vitro and to screen compounds for their effects on gene
expression in
vitro. The instant invention addresses these needs and more.
SUMMARY OF THE INVENTION
One aspect of the invention is a method for grouping test compounds into
classes, the method comprising:
(a) exposing a cell culture or cultures comprising at least two
gene-cell combinations to a test compound to generate an exposed cell culture
or
cultures;
(b) preparing RNA from the exposed cell culture(s);
(c) screening RNA from (b) for mRNA of each gene in the
gene-cell combinations of (a) to generate a gene expression fingerprint (GEF)
for the test
compound;
(d) repeating steps (a) - (c) for each test compound to be
grouped in classes; and
(e) comparing the GEF for each test compound (d), wherein the
test compounds are grouped into at least two classes based on differences in
their GEFs.
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Representative test compounds in each class may be further tested for a
representative
activity or an activity of interest in vivo.
The at least two gene-cell combinations may, for example, comprise at
least two different genes, at least two different cell types, or combinations
thereof. In
some embodiments a gene or genes in the gene-cell combinations may comprise an
endogenous gene under control of its native promoter, a heterologous gene
under control
of a heterologous promoter, an internal negative control gene, wherein an
effect on the
mRNA level of the negative control gene in response to the test compound is
indicative
of a toxic effect of the test compound, or an internal negative control gene,
wherein the
effect on the mRNA level of the negative control gene in response to the test
compound
is indicative of a non-specific effect of the test compound.
Screening of the RNA may comprise PCR amplification using
oligonucleotide primers specific for each gene. In some embodiments, the RNA
is
optionally reverse transcribed into cDNA. In some embodiments, the screening
comprises hybridization of nucleic acid sequences specific for each gene to
the RNA or
cDNA of the exposed cell cultures. In further embodiments, the level of the
mRNA of
at least one gene in the at least two gene-cell combinations is quantitated.
In some embodiments of the invention, combinations of two or more test
compounds can be administered to the cell cultures to generate a GEF for the
combination.
A further aspect of the invention is a method of identifying one or more
genes for use in a gene-cell combination for grouping test compounds into
classes, the
method comprising:
(a) exposing host cells in vivo or at least one host cell culture to a first
reference compound;
(b) preparing RNA from the host cells in vivo or host cell culture of
(a); and
(c) comparing the RNA of (b) to RNA from host cells in vivo or a
control host cell culture not exposed to the first reference compound; wherein
at least
one gene having an mRNA level affected in response to the first reference
compound is
identified as a gene for use in a gene-cell combination for grouping test
compounds into
classes. The RNA of (c) may be compared to RNA from host cells in vivo or a
control
host cell culture, wherein the host cells in vivo or a control host cell
culture have or has
been exposed to a second reference compound, whereby a gene having an mRNA
level
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affected in response to the first reference compound but not the second
reference
compound is identified as having a response specific for the first reference
compound.
A further aspect of the invention is a method for grouping test compounds
into classes, the method comprising:
(a) exposing a cell culture or cell cultures comprising at least
two gene-cell combinations to a test compound to generate exposed cell
cultures, wherein
at least one gene in the at least two gene-cell combinations is differentially
expressed in a
first and second reference state, to generate exposed cell cultures;
(b) preparing RNA from the exposed cell culture or cultures;
(c) screening RNA from (b) for mRNA levels of each gene in
the gene-cell combinations of (a) to generate a gene expression fingerprint
(GEF) for the
test compound;
(d) repeating steps (a) - (c) for each test compound to be
grouped into classes; and
(e) comparing the GEF for each compound tested in (d);
wherein compounds are grouped into at least two classes based on
differences in their GEFs. In some embodiments at least one of the first and
second
reference states is a disease state such as cancer.
In another aspect, the invention provides a method of generating a
reference gene expression fingerprint (GEF) for at least one reference
compound for use
in grouping test compounds into classes, said method comprising:
(a) identifying at least two gene-cell combinations, each of said
at least two gene-cell combinations comprising a unique combination of a
particular gene
and a cell of a particular cell type, wherein a first gene-cell combination is
identified by:
(i) exposing host cells in vivo or a host cell culture of a first
cell type to a first reference compound;
(ii) preparing RNA from the exposed host cells in vivo or
the host cell culture of (ii);
(iii) comparing the RNA of (ii) to RNA prepared from host
cells in vivo or a host cell culture of the first cell type not exposed to the
first reference
compound, wherein a change in a level of mRNA for a gene in cells of the first
cell type
in response to the first reference compound identifies the gene and cells of
the first cell
type as the first gene-cell combination for grouping test compounds into
classes; and
wherein a second gene-cell combination is identified by:
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(iv) exposing host cells in vivo or a host cell culture of the
first cell type or a second cell type to the first reference compound;
(v) preparing RNA from the exposed host cells in vivo or
the host cell culture of (iv);
5 ~ (vi) comparing the RNA of (v) to RNA prepared from host
cells in vivo or a host cell culture of the same cell type as in (iv) not
exposed to the first
reference compound, wherein a gene having an mRNA level changed in response to
the
first reference compound is identified as a gene for use in the second gene-
cell
combination for grouping test compounds into classes, said second gene-cell
combination
being different from said first gene-cell combination and comprising the
identified gene
and cells of the same cell type as in (iv); and
(b) screening RNA of (ii) and (vi) for mRNA for each gene in
each of the at least two gene-cell combinations to generate a reference GEF
for the first
reference compound for use in grouping test compounds into classes.
In another aspect, the invention provides a method for grouping test
compounds into classes, said method comprising:
(a) generating a reference ci~r for a reverence compouna
according to the method described immediately above and discussed below;
(b) generating a GEF for each test compound to be grouped into
classes by:
(i) exposing a cell culture or cultures comprising the at
least two gene-cell combinations identified in claim 1 to a test compound to
generate an
exposed cell culture or cultures;
(ii) preparing RNA from the exposed cell culture or
cultures of (i);
(iii) screening RNA of (ii) for mRNA of each gene in
each of the at least two gene-cell combinations of (i) to generate a GEF for
the test
compound;
(iv) repeating (i) - (iii) for each test compound to be
grouped in classes to generate a GEF for each said test compound; and
(c) comparing the GEF for each test compound generated in (b)
with the reference GEF of (a), wherein the test compounds are grouped into at
least two
classes based on differences or similarities between their GEFs and the
reference GEF.
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BRIEF DESCRIPTION OF THE FIGURES
Figure 1 comprises Figures lA and 1B. Figure lA is a graphical
depiction of GEF results for a reference compound (Ref) and test compounds x,
y, z in
two assays. Figure 1B depicts GEF results for a Reference (Ref) compound and
seven
test compounds in three assays. Each of the squares represents the results of
one assay.
Activity of a compound in a particular assay is indicated by a solid square.
Inactive
compounds are indicated by an open square.
Figure 2 comprises Figures 2A and 2B. Figure 2A depicts GEF results
for a Reference (Ref) compound and six test compounds in five assays. Figure
2B is a
single linkage tree diagram showing the percent disagreement between the
reference and
six test compounds with the GEF activity results depicted in Figure 2A.
Figure 3 comprises Figures 3A-3C. Figure 3A shows consensus GEFs for
human breast cells from normal and different stages in malignant progression.
Consensus gene expression changes representative of all of the cell lines
classified as
either weakly or highly invasive are graphically depicted. The values
correspond to the
median fold-change relative to the MCF10A reference observed for each gene
from data
in Tables 7A-7B. The data shown for the "normal" GEF are changes in gene
expression
observed in the 76N MEC strain relative to MCF10A. Genes with expression
changes
that are "tumor-associated" are represented by bars with left-handed stripes
(bars having
a stripe angling downward from left to right), genes associated with weakly
invasive
cancers have solid bars, and genes associated with highly invasive cancers
with right-
handed stripes (bars having a stripe angling upward from left to right). The
stippled bars
denote genes whose direction or extent of expression change is associated with
either
weakly or highly invasive cancers. The figure legend to the right of the three
graphs
lists the genes depicted. Each number on the legend identifies a particular
gene.
Figure 3B shows GEFs of two breast cell lines with unknown invasive
activity. Changes in gene expression of the breast fibroadenoma cell line
006FA2B and
the breast epithelial cell line HBL100 relative to MCF10A were determined
using Atlas I
cDNA hybridization arrays. Data are shown for the 28 genes shown in the figure
legend
in Figure 3A. The graphical representation of a particular bar (left-handed
stripe, right-
handed strip, stippled, or solid) has the same meaning as set forth above for
Figure 3A.
Figure 3C depicts GEFs for tumor biopsy specimens. Gene expression
was monitored by analysis of tumor RNA using Atlas I cDNA hybridization
arrays.
Changes in gene expression relative to a normal breast tissue specimen for the
28 genes
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listed in the figure legend of Figure 3A are shown. The graphical
representation of a
particular bar (left-handed stripe, right-handed strip, stippled, or solid)
has the same
meaning as set forth above for Figure 3A.
Figure 4 shows gene expression changes following treatment of MDA231
with various compounds. MDA231 cells were exposed to taxol, butyrate,
mevastatin, or
vehicle control for 72 h and analysed for effects on gene expression as
described in
M&M. The data shown correspond to effects on mRNA levels elicited by drug
treatment relative to control for those genes that had greater than 2-fold
changes in
expression in at least one treatment condition.
DETAILED DESCRIPTION OF THE INVENTION
I. Overview of Methods
The instant invention is directed to screening methods that allow the
grouping of compounds into classes of compounds with similar activity(s), as
measured
by the changes elicited by the compounds in the expression of certain genes in
certain
cells. There is no requirement that the certain genes or cells employed in the
analysis be
identified by function, map location, or other parameter physiologically
relevant to a
disease or indication for which a therapeutic drug is intended or sought.
Typically, a reference "gene expression fingerprint" (GEF) is first
generated for a reference compound or "state". A GEF is then generated for
each test
compound of interest as a result of the screening process of the invention.
The test
compounds are then grouped into classes on the basis of comparison with the
reference
GEF.
The basic screening process used herein to generate the reference GEF or
to screen test compounds relies on the use of "gene-cell combinations" . A
"gene-cell
combination" as used herein refers to a particular gene in a particular host
cell type.
Different gene-cell combinations can arise from various combinations of
particular genes
and particular host cell types, such as the same gene in two or more host cell
types, two
or more different genes in the same host cell type, and so on. In addition, a
single host
cell may comprise one or more such genes to generate two or more gene-cell
combinations .
A host cell type as used herein refers to a cell of a particular source, such
as but not limited to tissue of origin, state of differentiation, adaptation
to particular
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growth conditions, clonal variants, cell line, transformation, transduction,
viral infection,
parasite infection, bacterial infection, transgenic host, species of origin,
and so on.
Thus, for example, in an embodiment a reference GEF is generated for a
reference compound by exposing a cell culture or cultures comprising at least
two gene-
s cell combinations to the reference compound and observing a change in the
mRNA
levels) of the genes) in the gene-cell combinations in response to the
reference
compound. In a preferred embodiment of the invention, a single gene-cell
combination
is considered insufficient to generate a GEF. More typically, several gene-
cell
combinations (also termed herein "assays") are examined in response to the
reference
compound or in comparison of reference states to generate a "reference GEF" .
In yet a further embodiment of the invention, the relative mRNA levels of
at least one gene are compared in at least two host cell sources, wherein each
host cell
source comprises a different reference state to generate a reference GEF for a
reference
state. As discussed herein, the genes are chosen on the basis of being
differentially
expressed in a first and second reference state. Typically, at least one of
the reference
states is a disease state.
In the screening of test compounds by the methods of the invention, test
compounds or agents, such as libraries of peptides, peptidomimetics (such as,
but not
limited to p53, estrogen, raloxifene, tamoxifen, or IFN/3 mimetics),
polypeptides,
proteins, ribozymes, nucleic acids, oligonucleotides, or other organic or
inorganic
compounds, or natural products (e. g. , microbial broths, plant or animal cell
extracts) are
subjected to a screening process in which a GEF is generated for each test
compound by
exposing a cell culture or cultures comprising at least two gene-cell
combinations to each
compound and observing any changes in the mRNA levels) of the gene{s) in the
gene-
cell combinations in response to the test compound. The results are used to
compare
similarities and differences among the test compounds screened. Based on these
similarities or differences, the test compounds are divided into groups for
further
analysis. Such further analysis may involve in vivo testing or further
screening in other
assays.
In some embodiments of the invention, the methods of the invention are
useful to identify compounds or agents that, for example, are mimetics of
protein
function (e. g. p53-induced changes in gene expression) or modulate a disease-
associated
GEF in the direction of an unaffected GEF (e.g., neoplastic vs. "normal",
atherosclerotic
plaque vs. "normal" blood vessel, inflammatory tissue vs. "normal" tissue). In
such
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cases, the "reference GEF" is preferably derived from the differential gene
expression
patterns observed between different cell states (e, g. , p53 positive vs.
negative; metastatic
vs. non-malignant tumors) and not necessarily from treatment with a reference
compound
per se.
II. Reference Compounds and States
As used herein, the reference compound may comprise a protein,
polypeptide, peptide, nucleic acid, peptidomimetic, ribozyme, nucleic acid,
oligonucleotide, or other organic or inorganic compound, or microbial, plant,
and animal
natural products. The reference compound is preferably chosen as having a
representative in vivo activity, such as, but not limited to, inhibition of
cell growth,
stimulation of a receptor of interest, catalysis of a compound of interest,
synthesis of a
compound of interest, inhibition of replication of a virus of interest,
stimulation of cell
growth, inhibition of cell invasion of extracellular matrix, chemotactic
response, anti-
metastatic activity, anti-atherosclerotic activity, anti-inflammatory
activity, anti-apoptotic
effects, prevention of atherosclerotic lesion progression, decreased bone
loss, decreased
inflammation in rheumatoid arthritis, improved cognitive function, or
prevention of hot
flushes. However, the GEF generated for the reference compound need not
directly be a
measure of such activity. Rather, the GEF need only be representative of the
effect on
mRNA levels of the reference compound in a given gene-cell combination, or set
of
gene-cell combinations. Furthermore, the genes assayed for mRNA levels need
not be
directly or indirectly involved with the desired in vivo activity. In the
screening methods
of the invention, test compounds are screened to allow grouping into classes
relative to
the reference compound. Members of such classes can then be screened for the
desired
in vivo activity, lack of side effects, or other improved features.
One of ordinary skill in the art will typically understand that a reference
compound is chosen on the basis of the problem to be addressed. Thus, in
general, to
practice the methods of the invention a reference drug, chemical compound,
protein,
peptide, oligonucleotide, etc. that has a known or predictable physiological
effect
relevant to a pathological state or desired pharmacologic property is selected
as a basis
for identification of a class of compounds.
Some exemplary reference compounds include but are not limited to
tamoxifen, raloxifene, interferon a (IFNa), interferon /3 (IFN(3), interferon
'y (IFN~y), or
an anti-Ha-ras-ribozyme (Kijima et al., Pharmacol. Ther. 68:247-267 (1995));
ligands
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for nuclear receptors that are transcription factors, such as steroid
hormones, retinoids,
etc. ; receptors such as endothelin; ligands for transmembrane receptors, such
as
endothelin, gastrin releasing peptide, neuregulin, PDGF, cytokines,
chemokines, and
insulin; extracellular matrix components such as vitronectin, laminin, and
collagen; cell
5 adhesion molecules such as~N-CAM or I-CAM; inhibitors or activators of an
enzyme of
interest, such as L-NAME for nitric oxide synthase; chemotherapeutic agents,
such as
cisplatin or taxol.
A reference compound can also be the product of a gene expressed within
a host cell. Such genes may be endogenous or heterologous, under the control
of an
10 endogenous or heterologous promoter, etc. Exemplary genes include, but are
not limited
to transgenes, viral genes, antisense nucleic acids, ribozymes, etc.
In some cases, a reference state will be employed instead of, or in
conjunction with, a reference compound for the determination of the reference
GEF.
The differences in mRNA levels between two or more cells or tissues
representing
relevant physiological/pathological states form the basis of a reference GEF.
Some
examples of reference states include, but are not limited to, normal vs.
atherosclerotic
blood vessels of varying lesion severity; normal vs. progressive stages in the
development of malignant carcinomas, sarcomas, melanomas, or lymphomas; normal
vs.
stages of neurodegeneration associated with different types and severity of
Multiple
Sclerosis, Alzheimer's or Parkinson's disease.
III. Gene-Cell Combinations
A. Genes
The instant invention utilizes changes in the mRNA levels of one or more
genes in at least two gene-cell combinations, wherein the mRNA level of the
genes) is
responsive to the reference compound, to generate a GEF for each test compound
screened. The test compounds may affect mRNA levels directly or indirectly,
by, for
example, binding to a promoter or other regulatory element, binding to a
receptor and
triggering some intracellular signal, altering the stability of the mRNA,
binding to an
intracellular enzyme, such as a kinase or phosphatase, binding to a
transcription factor,
altering the redox environment, or affecting ion flux into and within the
cell. The genes
are preferably endogenous genes under the control of their native promoters.
In some
embodiments, cells may be infected with viruses, wherein the responsive genes
are viral
genes. In some embodiments, a marker gene, such as a heterologous gene under
control
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of a heterologous promoter, is introduced into the cell as an internal control
for
monitoring gene expression or the physiological state of the cell.
The set of one or more responsive genes for screening may be determined
in many ways. For example, the mRNA from a cell culture exposed to a reference
compound can be compared to mRNA from a control, or unexposed cell culture. In
some embodiments, an organism or animal is exposed to a reference compound in
vivo,
and the organism, tissue samples, explants, primary cultures, or the like used
as the
source for mRNA. Changes in the level of specific mRNA that occur in response
to the
reference compound can be identified by a variety of means, including but not
limited to
subtractive hybridization using either normalized or unnormalized libraries
(e.g.,
Gurskaya et al. , Anal. Biochem. 240:90-97 (1996), Bonaldo et al. , Genome
Res. 6:791-
806 ( 1996)), the use of multiple arrays made with EST's or cDNAs (e. g. ,
Bernard et al. ,
Nucl. Acids Res. 24:1435-1442 (1996); Schena et al., Science 270:467 (1995)),
DD-
PCR (Liang et al. , Science 257:967-971 ( 1992)), SAGE (Velculescu et al. ,
Science
270:484 ( 1995)), etc.
Although it is not required for the instant invention that the responsive
genes be responsible for any desired in vivo effect of the reference compound,
it may be
advantageous to use responsive genes of known identity and function. For
example,
genes known to be responsive to the reference compound may comprise all or
part of the
set of responsive genes. Such genes may be identified from the literature,
from cloning
of cDNAs from cell cultures exposed to the reference compound, or other
source. Thus,
for example, epidermal growth factor-regulated genes such as junB, rhoB, EGF
receptor,
integrin beta 1, and viculin may comprise all or a part of a set of genes to
screen
candidate compounds for selective EGF receptor agonists or antagonists. Genes
encoding
such proteins as p21, MDRl, hsp70, IGFBP-3, and bax have all been shown to be
regulated by p53 through different mechanisms. These genes may comprise all or
a part
of a set of genes to screen candidate compounds for p53 mimetics.
Preferably, a responsive gene chosen for use in the screening assay
sustains at least a two to fivefold change in the level of its mRNA in
response to the
reference compound. This change may be an increase or decrease. The measure of
five
fold or greater responsiveness provides for the detection of "weakly " active
test
compounds which may, for example, provide only a "partial" response (e. g. , a
two-fold
change in mRNA levels in comparison with a "full" response that is five-fold).
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In some embodiments of the invention, the same set of responsive genes,
or a subset thereof, or yet a different set, is examined in more than one cell
type as part
of the screening (i.e., to generate different "gene-cell combinations").
Preferably two to 15 or more gene-cell combinations (or "assays") are
used in screening compounds. The number of assays used to characterize
compounds or
reference states into groups based on GEF can be reduced using additional
reference
compounds with known in vivo effects. GEF's can be interpreted as like or
unlike the
reference compound or state. For example, when the additional reference
compound has
undesirable in vivo effects, assays which fail to distinguish the additional
reference
compound from the first reference compound may be eliminated from the
screening used
to generate GEFs. Some of the gene-cell combinations may be internal controls.
For
example, "house-keeping" genes such as GAPDH, actin, or cyclophilin are
typically
expected not to respond to the reference compound and thus can serve as
negative
internal controls. Positive internal controls can comprise, for example, a
recombinant
molecule under control of a promoter expected or known to be responsive to the
reference compound.
Additional internal controls can comprise genes which are predictive of
possible "toxic" effects of the reference or test compounds. For example, such
control
responsive genes include but are not limited to cytokines such as TNF or
lymphotoxin,
heat shock proteins such as hsp70, DNA damage inducible genes such as gadd153
or
gadd45, and the like. An increase in the mRNA level of one or more of these
genes is
typically predictive of a toxic effect of the reference or test compound.
Thus, for
example, in an embodiment, screening of test compounds for reduced toxic
effects is
accomplished by looking for reduced or unchanged levels of these internal
control genes.
B. Cells
Typically, a cell line and gene are chosen in concert as an "informative"
gene-cell combination for the screening of test compounds. Practical
considerations
include the tissue of origin of the cell line; the level of differentiation of
the cell line, the
level of expression of the target genes, the efficiency with which compounds
such as
cDNA, peptides, ribozymes, and so on can be taken up by the cell line, and so
on. In
some embodiments, tissue explants or clinical samples such as primary cell
cultures,
tissue explants from experimental animals, or clinical specimens such as blood
samples,
tumor biopsies, atherosclerotic blood vessels from a patient are preferred.
Thus, for
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example, although not a requirement in the instant application, it may be
advantageous in
the screening of compounds wherein the goal is to develop a new prostate tumor
therapeutic to use a prostate cell line.
IV . Screening methods
Typically, test compounds, preferably in the form of a library, are
screened against the set of responsive genes and cells to identify the
compounds with
identical or similar gene expression patterns. In an embodiment, for example,
a library
of about 105 -10' test compounds (e. g. , peptides, oligonucleotides,
ribozymes,
peptidomimetics, polypeptides, proteins, nucleic acids, oligonucleotides, or
other organic
or inorganic compounds, etc.) is screened. For example, a small molecule
library is
screened by exposing cell cultures to a typical final concentration of test
compound of 1 -
10 ~.M. A range of concentrations (e.g., low, medium, high) for each test
compound is
preferred to enable the detection of weakly active compounds and to help
distinguish
compounds which have different levels of activities at given concentrations.
For
convenience, the cell culture treatment may be in 96 well microtiter dishes.
Exposure is
typically done for a period of 24 to 48 hours, but can be as short as 30
minutes or as
long as a week, especially in the case of transfected or infected cells. The
cells are
usually treated in a humidified environment containing 5 to 10 % C02 at 37
° C, but
variations on these conditions may be warranted by the specific screen. RNA is
then
recovered from the exposed cultures by methods well known in the art,
preferably by a
method readily adapted to high throughput (e. g. , 96 well format) such as,
but not limited
to, poly dT capture plates (Mitsuhashi et al. , Nature 357:519-520 ( 1992)) or
silica gel-
based membrane adsorption purification (e.g., Qiagen's RNeasy Total RNA
Extraction
Kit). The mRNA may be optionally reverse-transcribed into cDNA. The mRNA or
cDNA can be used as probe or as target in hybridization reactions, and may be
immobilized or in solution. Messenger RNA from the set of one to twenty or
more
responsive genes can be quantitated by methods well known in the art using
such
exemplary techniques as standard Northern or slot blot hybridization, nuclease
protection, or quantitative PCR which are limited in the number of different
RNAs that
can be simultaneously analyzed as well as in their amenability to automation.
Other
preferred methodologies employ isotopically or fluorescently-labeled RNA or
cDNA
prepared from the isolated cellular RNA as hybridization probes for arrays
containing
purified cDNAs spotted onto membrane filters (e. g. , Bernard et al. , Nucl.
Acids Res.
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14
24:1435-1442 (1996)) or glass slides (Schena et al., Science 270:467-470
(1995)). A
modification of this general methodology utilizes chemically synthesized
oligonucleotides
covalently attached to a solid substrate instead of cDNAs as the target of the
hybridizing
RNA or DNA (Lockhardt et al., Nature Biotech. 14:1675-1680 (1996)). An
alternative
method directly measures the RNA or cDNA by hybridization with gene-specific
oligonucleotides, that can be differently labeled (e.g., with mass labels that
can be
quantitated by time-of flight (TOF) mass spectrometry; fluorescence enhancers,
such as
europium, terbium, samarium, and dysprosium, and the like (Xu et al. , Anal.
Chem.
Acta. 256:9-16 (1992)).
The GEF for each compound comprises the results of the screening
procedures. Compounds may be eliminated from further testing because of the
likelihood of toxic effects on the cell, nonspecific responses elicited, and
so on. The
GEF may be further modified by further testing with additional responsive gene
- cell
combinations , by using the same set of responsive genes and cells but
different
concentrations of test compounds, eliminating uninformative responsive gene-
cell
combinations from the GEF, and so on.
VI. Grouping Test Compounds into Classes
Test compounds screened as discussed above are then sorted into classes
based on their GEFs. For example, test compounds which elicited a change in
mRNA
levels of all members of a set of responsive gene-cell combination would be
grouped
separately from test compounds which elicited a change in only one instance,
two
instances, etc. As the number of assays used for screening increases, more
grouping
becomes possible.
Thus, for example, the reference compound is defined as being "active" in
all GEF assays; activity can be an increase or decrease, relative to control,
in the mRNA
level for the particular gene following compound treatment. . A compound x or
y is
discovered or identified by having activity in at least one GEF assay.
Compounds x and
y are categorized separately from the reference compound based upon inactivity
in at
least one assay .
Compounds are categorized with each other if they are active in the same
assays. In the simplest example employing two assays (see Figure lA), four
possible
categories of compound can be defined. The number of possible categories is
equal to
x°, where x is the number of activity states measured (e. g. + and -)
and n is the number
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of assays. In this example x" = 22 or 4 possibilities, represented by the
reference and
compounds x, y, z. Each compound is distinguishable from the others by a
different
GEF. The categories can be further refined by considering quantitative
differences in the
response to different compounds as a criterion for classification.
5 By increasing the number of GEF assays that are evaluated, more
categories of compounds can be defined. Compounds that are active in the same
assays
are categorized together. In the example in Figure 1B, where x" = 23 or 8
possibilities;
the seven compounds (x, y, z, a, b, c, d) are representative of different
categories. In
situations where there are three or more assays (e.g., Figures 1B, 2A, and
2B),
10 clustering algorithms can be used to determine the similarity of each
compound to the
reference compound and to each other. Initially, compound categories can be
determined
by their linkage distance, which is a measure of the percent of disagreement
with the
reference. When a compound shows a high percentage of activity matches with
the
reference, the closer the linkage distance is between a compound and the
reference. By a
15 simple clustering algorithm based on similarity to the reference, the
compounds shown in
Figure 2A would be characterized by the linkage diagram in Figure 2B. In this
analysis,
compound z is closest to the reference (i. e. linkage distance of 0.4) and
compounds a and
x are at equivalent distance. By changing the criterium for categorization to
a linkage
distance of 0.6, both of these compounds could be categorized with z. Thus,
the
stringency of the categorization can be adjusted by changing this linkage
distance. Use
of smaller linkage distances as the criteria for categorization would result
in the
generation of more categories than those obtained using greater linkage
distances.
Depending upon the data set, additional algorithms can be used to cluster the
compounds
based upon similarity to each other (James, M., Classification A1 org ithms
(1st ed.) New
York, NY, John Wiley & Sons (1985)).
The compounds with activity in only one assay (or less than 20% of the
assays, when there are greater than eight assays) are not categorized or
further evaluated
unless they are active in assays that form the basis for the majority of the
active
compounds identified (indicating that they may be affecting a portion of the
same
signaling pathway). For example, in Figure 2A compounds y and b would be
potential
candidates for further evaluation because they are active in assays that
identify
compounds x, a, and z. Compound c would not be further tested.
The decision to increase the stringency for categorization can be influenced
by the pattern of gene expression observed as well as data from other assays.
For
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example, in Figure 2A if evaluation of compounds x, z, and a revealed that
only x and z
were active in an important cell-based assay, compounds such as b and y which
demonstrate activity in assays common to x and z would be further evaluated
alone and
in combination.
VII. Further Evaluation of Test Compounds
After grouping of the test compounds into classes on the basis of GEF,
representative compounds can be further characterized in cell-based assays
well known in
the art for properties of interest. Such assays might include, for example,
inhibiting or
stimulating effects on cell growth, anti-viral activity, gel electrophoretic
mobility shift
assays with DNA-protein complexes prepared from extracts of treated cells,
cell invasion
through extracellular matrix or reconstituted basement membrane, anchorage-
independent
growth, chemotaxis, apoptosis, differentiation, cell adhesion to various
substrata, cell-cell
interactions, secretion, proteolytic activity, osteoclastic bone resorption,
etc.
It is advantageous in some instances to extend the cell-based assay to
animal models where available. Some examples of animal models known in the art
include animal models for uterotropic effects (e.g., uterine hypertrophy;
Allen-Doisey),
fever (e. g. , rabbit pyrogenicity), osteoporosis {e. g. , rat cortical and
trabecular bone
density following ovariectomy or transgenic/knock-out animals),
atherosclerosis (e. g. ,
lipid deposition in blood vessels of rabbits fed lipid-rich diets or in
transgenic/knock-out
animals), restenosis (e. g. , neo-intimal thickening following carotid
injury), cancer (e. g. ,
tumor induction in rats or mice, tumor xenograft growth in nude, athymic or in
transgenic/knock-out mice), metastasis (e. g. , lung colonization following
tail vein
injection of tumor cells), rheumatoid arthritis (e.g., adjuvant-induced joint
swelling),
multiple sclerosis (e.g., EAE model in marmosets or rats, transgenic/knock-out
mice),
Alzheimers disease (e.g., transgenic/knock-out mice).
In some embodiments, the GEF's of two or more test compounds may
complement each other, i. e. , when the GEF's are superimposed they
approximate that of
the reference compound or desired aspects of the GEF of the reference
compound. In
those instances the two or more test compounds may be used together in
combination in
cell-based or in vivo assays to determine whether the combination has desired
bioactivity.
The following examples are included for illustrative purposes and should
not be considered to limit the present invention.
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EXPERIMENTAL EXAMPLES
I. Selective Estrogen Compound Discoverv
A. Background
Epidemiological and experimental data support a protective role for
estrogen in reducing the incidence and severity of coronary artery disease,
Alzheimer's
disease, and osteoporosis. Estrogen treatment can, however, lead to unwanted
effects
such as endometrial hyperplasia in women and reduced testosterone levels in
men.
Therefore, the aim of the studies described here was to determine whether an
in vitro
profile for compounds with selective in vivo protective effects on bone (e. g.
, reducing
bone loss), neuronal function (e.g., anti-Alzheimer's disease), and the
vascular system,
(e. g. , anti-atherosclerotic) could be identified. Such selective compounds
would
preferably be devoid of undesirable side effects (e.g. > uterotropic effects
in females;
testosterone-lowering and decreased sex organ weight in males).
The research strategy we have pursued relies on three basic assumptions:
IS these "estrogenic" biological effects are mediated, at least in part, by
the estrogen
receptor (ER), which is a ligand-inducible transcription factor (Mangelsdorf
et al. , Cell
83:835-839 (1995)), regulation of gene expression by estrogen occurs by a
limited
number of mechanistically different processes that may be further modified in
a
tissue-specific manner, and compounds that have selective in vivo effects will
elicit
distinguishable gene expression patterns.
Available methods for identifying ER ligands that have potential as
selective drugs in vivo include standard ER ligand binding and cell-based
estrogen (E)-
dependent proliferation assays, or ER-mediated transactivation assays (e. g. ,
Tzukerman
et al., Mol. Endo. 8:21-30 (1994)), which utilize different E-responsive
promoters to
characterize compounds. Screening for ligands that differ in their abilities
to change ER
conformation is possible using a proteolytic fragmentation assay (Beekman et
al. , Mol.
Endo. 7:1266-1274 (1993)). Prudent use of these assays can permit the
separation of E
agonists from partial agonists and antagonists. However, these methods do not
provide
sufficient information about a compound to enable prediction of in vivo
selectivity since
compounds with markedly different in vivo effects are not distinguishable by
those
assays.
A method to classify compounds based upon differential gene expression
modulation was developed herein to identify such selective compounds. A total
of
forty-nine compounds was tested by this method and thereby categorized into
classes
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18
based upon their GEFs. Finally, the in vivo activities of some of the sorted
compounds
were evaluated to determine the predictability of the in vitro "fingerprint"
for in vivo
effects.
B. Specific Strategy
1. Genes and Cells
Known E-responsive genes were identified by literature search (52kD
cathepsin D, growth hormone, prolactin, progesterone receptor, pS2, TGFalpha,
IGFBP-1, CBG, Amphiregulin, TRHR (thyroid releasing hormone receptor)) and the
corresponding cDNA {or fragments thereof) were cloned and probe fragments
prepared
for Northern or slot blot hybridization studies by techniques known in the
art.
Mammalian cell lines that contain endogenous ER were identified through
literature
reports {GH3 pituitary adenoma, BG-1 ovarian carcinoma, MCF7 breast carcinoma,
ZR75-1 breast carcinoma, MDA361 breast carcinoma, Ishikawa human endometrial
carcinoma (Nishida et al., Acta Obstet. Gynaec. Jpn. 37:1103-1111 (1985)))
and/or by
analysis for ER expression (e.g., protein by Western blot analysis; RNA by RT-
PCR).
In addition, transfected cells which stably express ER were also tested
(MDA231-ER--breast carcinoma (Zajchowski et al., Cancer Res. 53:5004-5011
(1993)),
185B5-ER--human mammary epithelial cell line (Zajchowski et al. , Mol.
Endocrin. 5:1613-1623 (1991)), HepG2-ER--human heptocellular carcinoma, and
Fe33--rat hepatoma (Kaling et al., Mol. Cell. Endo. 69:167-178 (1990))).
The first step was to determine which of the genes and cell lines actually
showed measurable responses to E treatment. To that end, ER-positive cells
were grown
in estrogen-free culture medium and treated with the natural hormone, 17(3-
estradiol
(E2), or 17a-ethinyl-estradiol (EE; non-metabolizable estrogen) for short
(3h),
intermediate (24h), and long (72h) time periods and RNA prepared from the
cells at each
time point. Analysis of the levels of mRNA for the genes of interest gave an
estimate of
the kinetics of the response to EE treatment and an indication of the optimal
conditions to
measure the responsiveness of each gene.
2. Grouping of active specific compounds accordine to GEF:
selection of "informative" assays
At this stage, all of the identified E-responsive gene-cell combinations
could have been employed in a screen of a large number of compounds. However,
for
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19
this concept validation experimentation, we decided to simplify the GEF screen
by asking
whether a subset of these gene-cell combinations would be sufficient to
identify known
pharmacologically different compounds. To do this we chose to test those
gene/cell
combinations that responded to E2 or EE treatment with at least three-fold
effects on
mRNA level with seven additional compounds. The seven other compounds were
chosen
based upon known properties in in vitro and in vivo assays. Important
compounds were
tamoxifen (the 4-OH-tamoxifen (HT) derivative was used in the initial studies)
and
raloxifene (Ral) because at the time these studies were carried out, no
reported in vitro
method distinguished them even though they were clearly different in their in
vivo
responses (e. g. , although they have comparable anti-estrogenic effects on
the mammary
gland, tamoxifen is significantly more uterotropic than is raloxifene (Sate et
al. , FASEB
J. 10:905-912 (1996)). These compounds therefore became additional reference
compounds in the analysis, since we wanted to find compounds similar to them
as well
as different ones . We also chose a compound structurally related to estradiol
(i. e. , 2-
OH-17~i-estradiol (2HE)), other reported partial agonist-antagonists (i. e. ,
RU39411 (RU):
Gottardis et al.; Cancer Res. 49:4090-4093 (1989); 119010 (119): Nishino et
al., J.
Endocrinol. 130:409-414 (1991); centchroman (Cen): Hall, BBRC 216:662-668
(1995)),
and a pure antagonist (i. e. , ICI164384: Wakeling et al. , J. Endocrinol.
112:87-810
( 1987)) for these initial studies in order to determine whether compounds
with different
in vivo actions would be distinguishable using any of these assays.
Since 1.0 ~.M concentrations of compound were shown to elicit a maximal
response in most of the assays, all compounds were tested at 1.0 ~.M. In some
cases, 10
,uM concentrations were also tested. The ability of a compound to alter steady
state
levels of mRNA corresponding to each gene was quantitated by Northern, slot
blot, or
RT-PCR analysis as described herein (Table 1). The average fold-increase in
mRNA
levels elicited by either E2 or EE for each gene/cell assay is provided in the
third
column. Compounds that elicited a response in a particular assay are
designated with a
(+); those that showed no effect are designated with a (-). Analysis of 27
different
genelcell combinations with these nine compounds (to generate a GEF for each
compound) revealed that most of the assays provided redundant information
(seen as the
same pattern of activity across the series of compounds in Table 1); but, five
distinct
activity patterns across this set of compounds were discernable among all
these gene/cell
combinations, as indicated by the roman numerals I-V on the rights side of
Table 1. It is
of interest that pattern I is found in most of the cell types tested, but the
other patterns
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(particularly pattern II) may show a cell-type preference. Such data
emphasizes the value
of using different cells as well as different genes in carrying out these
analyses. Also
evident in Table 1 is the fact that compounds can have differential abilities
to activate the
same gene (e.g., 52kD) depending upon the cell (e.g., ZR75-1 compared to BG-
1).
Table 1. ACTIVITY of SELECTED ER LIGANDS In MODULATING GENE E7(PRESSiON
Gene Ceil Fold Ettect+EE2~ 2HEHT R~ 119Cen ICI
Line . .~._.-..HJ -
_
GH3 12 rid+ + - _ - _ _ _ I
~F7 10 + rid+ - - - ridrid - l
FH BS~ER > 2 0 + + + - - _ _ _ - i
Ra ZR75-1 15 + + + - - - - - - I
iii Ishiicawa15 + rid+ - - rid rid- - I
Fib I3G-1 2 5 + + + _ - _ _ _ _ I
5 rid+ + - - - ridrid - I
52kD MCFT 5 + rid+ - - - ridrid - I
52kD E35~ER 9 + + + - - - - - - I I
IGFBP-1HepG2-ER15 + + + - - _ _ _ _ I
pS2 MOA361 10 + + + - - - - rid - I
10 + rid+ - - - ridrid - I
I
pS2 BCr1 >20 + + + - _ _ _ _ _
AmphiregMDA361 8 + + + - - - - rid - I
52kD BG-1 5 + + + - _ _ _ _ _ I
BG-1 3 + + + - - _ _ _ _ I
pS2 ZR75-1 5 + + + - - - - - - I
5 rid+ + + + + + + - I
PRL GH3 3 9 + + + + + + + + - I I I
pS2 MDA-ER -450 + + + + + + + + - 1
TGFalphaB5-ER 10 + + + + - + - + - I
TGFaiphaMOA-ER 11 + + + + - + - + - I
52kD ZR75-1 6 + + + + - + - + - I Iii
C HepG2-ER24 + + + + - + - + - I
~pS2 BS~ER >20 + + + + - + - + - I
FR MDA-ER -500 + + + + - + + + - IV
IGFHP-1Fe33 12 + + - + + + - - - V
Summary of the maximal responses of each geneiceil combination (i.e. assay) to
compound
treatment. +, active compounds: -, inactive compounds: rid, not detertnved.
The cell lines
listed were treated with the indicated compounds. tota~ RNA was isolated. and
analysed for
modulation of expression of the listed genes as described in M8~M. The maximal
average
gene expression response of each cell line following E2 or EE treatment is
provided in the
third column ti.e. Fold Effect + E). Each assay can be grouped according to
their response
to compound treatment mto the classes shown at the right side (i.e. I-V).
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Furthermore, the same compounds can have different activities on different
genes within
the same cell (e.g., PR compared to pS2 or TGF-a in the MDA-ER cells).
Thus, for this selected compound set, five non-redundant "informative"
assays, i.e, those whose combined use enable the discrimination of compounds
into
S different classes were identified in the twenty-seven assays analyzed. It is
noteworthy
that not all five assay types (patterns) were equally represented. The
predominant assay
type showed responsiveness only to estradiol derivatives (i. e. EE and 2HE)
whereas the
least frequently identified patterns (corresponding to the assays that score
Ral and 119)
were observed only 4 times. Thus, of the estrogen response assays used herein,
a subset
thereof chosen randomly would comprise at least 15 and preferably as many as
20 assays
for use in the GEF screen. The statistical probability of identifying
raloxifene as an
active compound in such a screen would be 96 % if 20 assays are employed, 91 %
if 15
assays are used, and 80 ~ if only 10 are analyzed (Snedecor et al. ,
Statistical Methods.
8th ed. Iowa State University Press, Ames, Iowa, Chapter 7, ( 1989)) .
To simplify the GEF screen, additional studies were performed to
determine which of the redundant assays was most amenable to screening
strategies (e.g.,
highest reproducibility and extent of change relative to control). The IGFBP-
1/Fe33
gene-cell combination (representing pattern V) was not employed in further
studies (due
to difficulties interpreting data in these liver carcinoma-derived cells,
where drug-
metabolizing activity is significant). The chosen representative assays for
subsequent
studies are shown in Table 2. This representation of the data shows that each
compound
is identified by a specific GEF based upon the activity elicited in each of
the four assays
(seen as + and - pattern of activity in the column underneath each compound).
In this
manner, compounds with identical GEFs were grouped together and were
distinguishable
from those with different GEFs. For example, E2, EE, and 2HE were placed in
one
group (#1 in Table 2) and HT and RU in another (#2). Of utmost importance was
the
observed difference between E2, Ral, and HT, which indicated that these assays
are
successful in discriminating among compounds with distinct in vivo
pharmacologies.
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Table 2. ER
LIGAND
CLASSIFICATION
by
GEF
Gene Cell Line E2 EE 2HE HT RU Ral 119 Cen ICI
PR BG-1 + + + - - - - - -
PRL GH3 + + + + + + + + -
TGFalpha MDA-ER + + + + + - - + -
PR MDA-ER + + + + + - + + -
Group 1 1 1 2 2 3 4 2 5
#
3. Classification of additional compounds using_selected end e/cell
assays
This method of classification was employed to separate an additional thirty
compounds, many of which are structurally related to the first nine compounds
tested.
Compounds E1 (estrone), E3 (estriol), DHE (17a-dihydroequilen), DHEN (17a-
dihydroequilenin), ZK182491 and ZK155843 are derivatives of either 17a-
estradiol (17a-
E2) or 173-estradiol. Compounds ZK166780, ZK166781, ZK167466, ZK167957, and
ZK180686 are 11 j3-substituted 17/3-estradiol derivatives related to RU39411.
Compounds
HT, ZK186275, ZK183819, ZK182956, and ZK183955 are tamoxifen derivatives.
Compounds ZK185157 and ICI182780 are related to the pure steroidal antagonist,
ICI164384. Compounds ZK182254, ZK186217, and raloxifene are benzothiophenes.
Compounds ZK183659, ZK22496, and ZK185704 are structurally related (i.e.,
contain a
cyclophenyl moiety). Compound ZK167502 is a napthalene derivative and
coumestrol is
a phytoestrogen (Price et al., Food Addit. Contam. 2:73-106 (1985)). Many of
these
had been previously classified as agonists, partial agonists, or antagonists
of the ER
through assays of ER binding and transcriptional activation. In these
experiments,
compounds were scored using three activity levels (i. e. , inactive, partially
active as
< 50% of the E2 response, fully active as > 50% of the E2 response). As is
evident
from Table 3, the compounds could be divided into ten groups by this analysis
(see Table
3). This separation of compounds is not based primarily upon chemical
structure as
indicated by the results with the compounds that are related to RU39411 (i. e.
,
ZK166780, ZK166781, ZK167466, ZK167957, and ZK180686). These six compounds
are split into 3 different classes based on their GEFs.
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Table 3. RESULTS OF GEF ANALYSIS
COMPOI~ID I PRIBG-1PRUGH3TGFalMDA-ERPRlMDA-ER Gf~'
I
E2 ++ ++ ++ ++
E ++ ++ ++ ++
E1 ++ ++ ++ ++
E3 ++ ++ ++ ++ 1
Coumestrol ++ ++ ++ ++
167502 ++ ++ ++ ++
2HE ++ ++ ++ ++
DI-E + ++ ++ ++
pHEhl + ++ ++ ++ 2
182491 + ++ ++ ++
155843 ++ + ++ ++
l7alpha-E2 ++ + ++ ++ 3
22496 ++ + ++ ++
188780 + + ++ + 4
188781 + + ++ +
RU394t 1 - + ++ + 5
HT - + ++ +
Centchroman - + + +
Tamox - + + +
188275 - + + + 6
182254 - + + +
185704 - + + +
183955 - + - + 7
119010 - + - +
Ralox - + _ _
186217 - + - - 8
183819 - + _ _
187486 - - . +
187957 _ . - + 9
185157 . _ _ +
180686 _ _ - +
182956 - - -
183859 _ _ _ -
IC1164384 - - - -
IC1182780 - - - -
progesterone - - - - 10
RU488 - - - -
resveratrol - - - -
dexamethason - - - -
phenol red - - - -
Data represent response to
the average (at concentrations tOuM)
maximal up of
-- at least experiments with Activity
three individual duplicate
determinations.
++, >SO~o E2: -,
+, <SO~o: inactive.
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4. Determination of the predictive ability of the GEF classification for
in vivo effects
Included in the compounds tested in the section above were standards (i. e. ,
E2, tamoxifen, raloxifene, ICI 164384) with reported distinguishable in vivo
profiles.
E2, tamoxifen, and raloxifene, but not ICI, have "estrogenic" effects on the
bone and
cardiovascular system in experimental and/or clinical studies (i. e. , they
are effective in
attenuating atherosclerotic lesion formation tamoxifen: Williams et al. ,
Arterioscler.
Thromb. Vasc. Biol. 17:403-408 (1997); raloxifene: Bjarnason et al.,
Circulation
96:1964-1969 (1997) and/or protecting against ovariectomy-induced bone loss
(tamoxifen: Love et al. , N. End J. Med. 326:852-856 ( 1992); raloxifene:
Black et al. ,
J. Clin. Invest. 93:63-69 (1994)). Yet, E2 and tamoxifen were readily
distinguishable
from raloxifene in their greater potency in eliciting uterotropic effects
(Sato et al. ,
FASEB J. 10:905-912 (1996) and Table 4), thereby implying that raloxifene has
tissue-selective actions in vivo. Through our analysis of gene expression
patterns, we
found that these four compounds have different GEFs that place them in
separate groups
(Tables 2 and 3). These data support the idea that compounds with selective in
vivo
effects be distinguished by different gene expression profiles (GEFs) in
vitro.
Of particular interest was the group of compounds including ZK167466
(Group 9, Table 3). Like the raloxifene group (Group 8), these compounds
exhibited
activity in only one GEF assay. To determine whether the co-classification of
these
compounds predicted similar in vivo pharmacology, they were tested in vivo for
uterotropic activity as well as their ability to reduce the loss in bone mass
caused by
decreased circulating levels of estrogens (i. e. , induced experimentally by
ovariectomy).
Table 4 compares the activity of this group of compounds to E2, Tam, Ral, and
ICI. All
four of the group 9 compounds were different from the others in both assays.
They
showed either no or only weakly stimulatory effects (depicted as - or -/ + in
Table 4) in
promoting endometrial thickening (i. e. , uterotropic effect) . Three of them
are
significantly effective in the "bone protection" assay that predicts efficacy
against
osteoporosis (Table 4). These data indicate that this GEF profile predicts a
novel
selective compound class (i. e. , one with bone-protective effects and little
or no
uterotropic response), which could not have been identified (separated from
the other
"partial agonists") with the existing in vitro screening methods.
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c c
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o
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CA 02317650 2000-07-06
WO 99/37817 PCT/US99/01552
26
C. Materials and Methods
1. Cell Culture and Compound Treatment
MDA-231 ER transfectant E-28 cells were routinely cultured in phenol
red-free alpha-modified minimal essential medium (MEM Gibco BRL; Gaithersburg,
MD) supplemented with 1 milliMolar (mM) HEPES, 2mM glutamine, 0.1 mM MEM
non-essential amino acids, 1.0 mM sodium pyruvate, 50 ~cg/ml gentamicin (all
from
Gibco), 1.0 microgram/milliliter (~,g/ml) insulin (Sigma; St. Louis, MO), and
5
DCC-treated FBS (Intergen) . Cells were plated at approximately 40 %
confluency ( 1.5 x
106/plate) in 150 mm culture dishes. Following an overnight cell attachment,
the
medium was changed to include 0.2 % ethanol or the test compounds and cultured
for an
additional 48 hours (h).
GH3 rat pituitary cells were routinely cultured in DMEM-F10 (I: i)
medium containing 12. 5 % horse serum, 2.5 % FBS, 25 mM Hepes, 2 mM L-
glutamine,
and 50 tcg/ml gentamicin sulfate at 37°C, 5% COZ. Under these
conditions, the cells
were partially adherent, and both adherent and non-adherent cells were
maintained during
the passaging of the cells. For the measurement of mRNA expression, cells were
seeded
(106/100 mm dish) in culture medium without phenol red and containing DCC-
treated
serum. After 3 days, the medium was changed to one containing 0.2 % ethanol or
the
test compounds, and the cells were further incubated for 2 days.
BG-1 human ovarian carcinoma cells (Geisinger et al., Cancer 63:280-288
( 1989)) were cultured in DMEM: F12( 1:1) medium containing 10 % FBS, 2 mM
L-glutamine and 50 ~,g/ml gentamicin sulfate. For the measurement of mRNA
expression levels, cells were cultured for 24h in phenol red-free medium
containing 5
DCC-treated FBS prior to plating in the same medium at a density of 2 x 106/
150 mm
plate. The following day, the medium was changed to include 0.2% ethanol or
the test
compounds and cultured for an additional 72h.
ZR75-1, MCF7, and MDA361 human breast carcinoma cell lines were
routinely cultured in alpha-modified MEM supplemented with 1 mM HEPES, 2 mM
glutamine, 0.1 mM MEM non-essential amino acids, 1.0 mM sodium pyruvate, 50
~.g/ml
gentamicin, 1.0 ~,g/ml insulin, and 10% FBS. Cells were plated (ZR75-1: 1.5 x
lOb/p100; MCF7: 2 x 106/p150; MDA361: 5 x lO6/p100) in phenol red and insulin-
free
media containing 5 % FBS-DCC for the assays. Following an overnight cell
attachment,
the medium was changed to include 0.2 % ethanol or the test compounds and
cultured for
an additional 24h (ZR75-1), 48h (MDA361), or 72h (MCF7).
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27
The HepG2 human hepatocarcinoma cells, stably transfected with ER
(clones ER1 and ER2), were cultured in EMEM (GIBCO), supplemented with 1 mM
HEPES, 2 mM glutamine, 0.1 mM MEM non-essential amino acids, 1.0 mM sodium
pyruvate, 50 ~,g/ml gentamicin, and 10% FBS. Ishikawa human endometrial
carcinoma
cells were cultured in EMEM with 2 mM glutamine, 50 ~,g/ml gentamicin, and 10
FBS. Fe33 (ER-transfected FTO-2B rat hepatoma cells) were maintained in DMEM-
Ham's F12 (1:1) without phenol red containing 10% DCC-FBS on 0.1 % gelatin
coated
Petri dishes. All cells were plated (HepG2-ER: 4 x 106/p100; Ishikawa: 2 x
106/p150;
Fe33: 2.5 x 105/p150) in phenol red and insulin-free media containing 5% FBS-
DCC for
the assays. Following an overnight cell attachment, the medium was changed to
include
0.2 % ethanol or the test compounds and cultured for an additional 72h.
The ER-transfected human mammary epithelial cells (BS-ER) were
maintained and assayed for gene expression changes according to protocols
previously
described (Zajchowski et al., Mol. Endocrinol. 5:1613-1623 (1991)). Compound
or
vehicle treatment was for 72h.
17~i-estradiol, 17a-ethinyl estradiol, estrone, estriol, progesterone,
dexamethasone, phenol red were purchased from Sigma Biochemicals (St. Louis,
MO).
All other compounds were synthesized at Schering AG (Berlin). Stock solutions
(i0
mM) of all the chemicals were prepared in DMSO and diluted in ethanol for the
assays.
2. RNA Isolation and Slot Blot Analyses
At the end of the compound treatment time, cell monolayers were
harvested into Ultraspec (Biotecx Laboratories, Houston, TX) or RNeasy (Qiagen
Inc. ,
Santa Clara, CA) RNA isolation reagent and processed according to the
manufacturer's
suggested protocol. Total RNA (MDA-231 ER:10 ~,g; GH3:1.0 ~,g) was spotted
onto a
Zetaprobe-GT nylon membrane using a 48-well slot blot apparatus attached to a
vacuum
manifold. Total RNA (20 fig) from treated and untreated samples of all of the
other cell
lines was evaluated by Northern blot analysis. Hybridization of the membranes
to
3zp_dCTP labeled probes was carried out as previously described. Quantitation
of the
specific hybridization in each spot by subtracting non-specific background
detected in a
negative control for each mRNA was performed using a Fuji phosphorimager; the
ratio
of the signal intensities in compound-treated samples relative to controls
provided the
value for fold-change used in the assessment of the compound activity for each
particular
assay. Changes in mRNA levels greater than or equal to 2-fold were scored as
positive.
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3. Progesterone Receptor Reverse Transcriptase-Po~merase Chain
Reaction (RT-PCR)
All RNA samples were diluted to 20 ng/p,l in DEPC-treated water. RT
PCR was performed using 100 ng total RNA. The reaction mixtures contained 5
units
rTth DNA Polymerase (Perkin Elmer; Foster City, CA), 1X EZ buffer (Perkin
Elmer;
Foster City, CA), 2.5 mM Mn(OAc)2, 300 ~,M dNTP's (mix from Pharmacia;
Alameda,
CA) and 10 pmol of each biotinylated primer in a final volume of 50 ~1. PCR
primers
PR#1 (5' GTC AGT GGR CAG ATG CTR TAT TT), PR#2 (5'-11C TTC AGA CAT
CAT TTC YGG AAA TTC) were synthesized by Synthetic Genetics (San Diego, CA).
Amplification consisted of a 30 minute RT step at 60°C immediately
followed by 33
cycles of a two step PCR reaction (95°C for 15 seconds, 60°C for
45 seconds) and a
final 7 minute extension at 60 C in a Perkin Elmer 9600. Following PCR, 1/20
reaction
volume is removed and quantitated using streptavidin-coated 96-well
microplates and
oligonucleotide probes specific for the PCR target. The probe is coupled to
either HRP
or AP and addition of either colorimetric (HRP) or chemiluminescent (AP)
substrates
permits quantitation of 300-500 initial copies of specific RNA template in a
20-I00 ng
total RNA sample. In vitro-transcribed PR mRNA was used to generate standard
curves
(calculated by non-linear regression analysis using a four parameter sigmoidal
plot) for
quantitation of the amount of PR mRNA in each reaction. Changes in mRNA levels
were scored as positive if they were greater than or equal to 3-fold.
4. Uterine Histomorphometric Analysis
For determination of uterotropic activity, immature, 19-21 day old female
Sprague-Dawley rats, weighing 35-50 g. were given daily subcutaneous
injections for
three days with compounds or vehicle alone. The compounds were dissolved in a
vehicle
consisting of 10 % ethanol in arachis oil or a mixture of
benzylbenzoate/castor oil ( 1:4) .
On day 4, the animals were weighed and euthanized by carbon dioxide
asphyxiation.
The uteri were excised and placed in neutral buffered 3.7 % formaldehyde for a
minimum
of 24 hours. The uteri were then embedded in paraffin, cut into 4-~,m
transverse
sections, and stained with hematoxylin and eosin and the sections evaluated
for luminal
epithelium cell height as described by Branham et al. (Branham et al. , Biol.
Re~rod.
53:863-872 (1995)). The difference in epithelial cell height between the
estrogen (0.3 ~cg
173-estradiol/animal) and vehicle-treated groups was calculated and expressed
as 100 % .
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29
The activity of the compound of interest as a percent of 17/3-estradiol was
calculated
according to the following formula:
x= 100% [height(test compound -height(vehicle)] .
height( 17 (3 -estradiol) -height(vehicle)
5. Bone Mineral Density Measurement
For determination of efficacy in preventing bone loss, 3 month old female
rats (Sprague Dawley) were ovariectomized (ovx) and treated immediately after
surgery.
Compounds were applied once daily s.c. in benzyl benzoate/castor oil (1:4) or
arachis
oiI/ethanol (95:5). Control groups {sham/ovx - treated with vehicle) and
treatment
groups consisted of 6 animals each. 4 weeks after surgery animals were
sacrificed and
the left and right tibia were processed for bone mineral density measurements.
Bone
mineral density (BMD) was measured in the secondary spongiosa of the proximal
tibia by
pQCT (peripheral quantitative computed tomography). Results are expressed in
percent
protection from bone loss. Bone protection was. expressed relative to the
effects of
estrogen {0.3 ~,g 17(3-estradiol/kg) according to the following formula:
x= 100% [BMD(test compound -BMD(vehicle)] .
BMD( 17 ~i -estradiol) -BMD(vehicle)
II. Screening for an Interferon-a (IFN/3) Mimetic
A. Back rg ound
IFN/3 has efficacy in the treatment of Multiple Sclerosis (MS) (The IFN(3
Multiple Sclerosis Study Group Neurolo~y 43:655-661 (1993)). The precise
mechanism
by which IFN~3 elicits its therapeutic efficacy is unknown. However, a great
deal of
knowledge exists concerning the signal transduction pathways modulated by
IFN~i; as a
ligand, IFN~3 directly interacts with its receptor to induce phosphorylation
of a number of
signal transducing proteins (STATs (Ihle, Nature 377:591-594 (1995)) and
eventually
direct specific changes in gene expression (Darnell et al., Science 264:1415-
1421
(1994)). A homologous member of the same family of cytokines, IFNa, is capable
of
binding the same receptor protein yet cannot be used in the treatment of MS
due to its
unacceptable side effect profile. Another interferon, IFN~y, shares some of
IFN~i's
effects on gene expression, yet actually exacerbates the symptoms of MS
(Panitch et al. ,
J. Neuroimmunol. 46:155-164 (1993)). Therefore, differences in the biological
effects of
CA 02317650 2000-07-06
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these three ligands can be exploited in developing screens to identify
selective IFN,Q
mimetics that might be more efficacious and have better tolerability than
IFN~3 itself.
Animal models to test drug efficacy in ameliorating the severity of this
disease exist (i. e. , Experimental Autoimmune Encephalitis (EAE) or T cell
transfer EAE
5 model).
B. Cell selection and gene identification
Cells employed in these studies can be representative of known or
suspected IFN~3-responsive tissues (e. g. , B cells (e. g. , Daudi), T cells
(e. g. , Jurkat),
10 glioblastoma (e.g., T98G), carcinoma (A549), and astrocytes (e.g., CH235)).
RNA is
prepared from candidate cell lines that have been treated with IFN~i and used
to estimate
the number of differentially expressed sequences by hybridizing probes
prepared from
this RNA on microarrays containing 100 or more pre-selected cDNAs, such as the
Atlas
cDNA Arrays (i. e. , Clontech). The cell lines that show the largest number of
15 differentially expressed sequences are chosen for studies to identify IFN(3-
responsive
genes. Technically, this can be approached through any available differential
gene
expression screening strategy (e. g. , DD-PCR, subtractive hybridization
libraries, etc. ).
Subsequent to identification of the differentially-expressed genes, limited
optimization is
preferred to determine whether conditions such as time of treatment can
enhance the
20 extent of mRNA change relative to control. Conditions amenable to analysis
of the
largest number of genes are used.
C. Assay characterization
For each cell line, genes that show significant regulation (preferably at
25 least a 5-fold increase or decrease from basal level) are used in screens
with a set of
compounds known to have different, but overlapping effects in common with
IFN~i (e.g.,
IFNa, IFN~y, IL-8, IL-12). This evaluation can be carried out by arraying the
cDNAs
for these candidate genes and using RNA isolated from each of the compound-
treated
cells to prepare hybridization probes. Responsive genes are evaluated for the
response to
30 each compound. An exemplary set of one or more genes, including gene/cell
combinations, responds only to IFN/3, another group of genes responds to both
IFNa and
(3, another with IL-8, IFNy, and IFN/3, etc.
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31
D. Assay selection
The "best" gene/cell combination (greatest fold response and
signal-to-noise ratio for detection; gene expression measurable in cell line
where other
"informative" genes are measured) from each group of genes is chosen for the
compound
screen. Internal control genes are designated in the cell line to be used as
indicators of
cytotoxicity (e.g., gadd45, hsp 70).
E. Screening
A test compound library is screened for those test compounds which are
specific modulators of IFN-responsive genes using a scoring method of active
and
inactive. The "active" hits are those that elicit changes in gene expression
significantly
above the background variance of the specific assay. Test compounds are then
grouped
according to their GEF and re-tested to determine the ECM for representative
compounds.
At this stage in the generation of a GEF that will be predictive for in vivo
efficacy, it may not be clear how close to the GEF of IFN,Q a "hit" will need
to be in
order to have IFN-like activity in vivo. To estimate this, test compounds that
showed
activity in the greatest number of assays (i. e. gene/cell combinations) are
tested in a cell-
based assay for IFN responses (e.g., anti-viral effects) prior to in vivo
testing. This
screen is employed as a way of sorting through GEFs to determine whether
"hits" with
activity in very few IFN-response assays have IFN-like activity. If none of
the hits that
are active in multiple GEF assays show activity in the bioassay, compounds are
preferably screened in combination with each other to determine their GEF upon
co-
treatment. Combinations of compounds that generate new GEFs closer to that of
IFN(3
are subsequently tested for in vitro activity in the bioassay.
Representative compounds are selected for in vivo evaluation based upon
their activity in in vitro bioassays, potency in the GEF assays, and other
available
information. If any "hits" meet criteria for in vivo testing, they are
evaluated for
efficacy in the EAE model. If not, additional compound sources can be
screened, or
weak "hits" can be optimized against their GEF to find more potent compounds
before
testing in animal models.
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32
F. Selectivity testing and "selective" GEF determination
The GEF profile determined in the previous step can be used directly as a
means of optimizing "lead" or representative best candidate compounds. At this
stage of
analysis, ECSOs and maximal responses for the derivative compounds for each
assay are
considered.
The "lead" compounds) is usually tested for adverse, undesirable effects
in appropriate biological models (e.g., induction of fever, testable in a
rabbit
pyrogenicity assay). If there are "lead" compounds that have different GEFs,
the GEF
corresponding to the "lead" which has little or no activity in this assay is
used for further
optimization. If, however, none of the "lead" compounds meet the selectivity
requirements for the desired drug, it may be necessary to incorporate
additional assays
into the screening panel and re-test all of the bioactive "hits"; in this new
screen,
compounds within the previously designated GEF classes may be differentiated
from each
other by these new assays (i. e. , due to a different GEF that is now
discovered). In that
case, additional in vivo evaluation is necessary to validate the
predictability of the new
GEF for in vivo efficacy and selectivity.
III. Identification of a n53 Mimetic for Cancer Treatment
A. Back round
Mutation or deletions of the p53 tumor suppressor gene are prevalent in
many human cancers {Hollstein et al., Science 253:49 (1991); Weinberg, Science
254:1138 (1991)). Studies during the last decade have elucidated the dominant
role that
this protein plays in maintaining the normal balance between cell
proliferation and death.
Most importantly, experimental evidence from both in vitro and in vivo studies
has
demonstrated the feasibility of p53 protein replacement as a treatment for
cancer (Wills
et al., Hum. Gen. Ther. 5:1079-1088 (1994)).
In addition to its transcriptional regulatory activities, p53 has been shown
to influence DNA replication and repair as well as apoptotic signaling
pathways. A
profile of the changes in gene expression that result from the expression of
wild type
(WT) p53 in a cancer cell will be used in the application presented here as a
tool to
search for compounds that mimic the activities of p53. The existence of
expression
systems that enable investigator-control of protein expression (e.g., lac or
tet-inducible
systems) as well as temperature sensitive (ts) p53 proteins and a number of
p53 mutants
enhance the suitability of this system for drug-screening efforts.
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33
B. Cells and Genes
Cancer cell lines which have been stably modified (e. g. , by transfection or
transduction techniques) to enable regulatable expression of the p53 WT or
mutant
variants are used to identify p53-dependent genes. These studies would
preferably be
performed in a p53 null cell background, although this criterion is not
absolute. Any of
the methods described in previous examples can be employed to identify
candidate p53-
responsive genes. RNA for this analysis is isolated from cells cultured under
conditions
where (1) the expression of the p53 protein is on or off (e.g., in an
inducible expression
system) or (2) the active vs. inactive form of the p53 protein is present
(e.g., for a
temperature sensitive p53 protein or for WT vs. mutant proteins).
In this example, ( 1 ) the effector compound is a 53kD protein (i. e. , p53)
and not a small molecule (i. e. , estradiol) or a polypeptide ligand (i. e. ,
IFN-Vii) and (2) the
search is for an alternative effector molecules) which elicits the same in
vivo effects as
p53, not a more selective or efficacious molecule. In this regard, it is
important to note
that a successful p53 mimetic could be a combination of compounds, each of
which
perform a "subset" of the essential p53 functions. In the previous instances,
the cell
lines) which showed the greatest number of changes in response to the
reference
compound was chosen for the identification of responsive genes. In this case,
a minimal
set of gene/cell readouts that are predictive of p53's tumor suppressive
function is the
desired outcome of the assay selection step. Therefore, the initial gene
identification
approach will evaluate several different tumor cell lines whose tumorigenicity
is
suppressed by p53 introduction/activation. The p53-responsive assays that are
shared by
all of these cells are selected for further evaluation.
C. Assay characterization and selection
An additional, but not essential, method for choosing the appropriate
assays is to evaluate the expression of candidate genes following induction of
the WT
p53 compared to its mutated versions. Genes which are regulated by truncated
or
mutated p53 proteins that retain their tumor suppressor function are useful in
a p53
mimetic screen since they are markers of desirable p53 functions; genes which
continue
to be regulated by mutant versions of p53 that are inactive in tumor
suppression would
be eliminated from the screen or used as "non-selective" assays. The choice of
assays to
be used as read-outs of "cytotoxicity" may differ in this screen from those
applications
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34
described above, since some of the targets of p53 may be genes like gadd45;
the assays
which do not respond to p53 can be retained as "cytotoxicity" readouts.
Evaluation of gene expression patterns elicited by compounds will be
similar to other searches. "Hits" will be grouped according to their GEF and
re-tested to
determine ECM for each active assay.
D. Preliminary cell-based assts
The "hits" can initially be tested in in vitro assays for proliferation (e.g.,
measured by 3H-thymidine uptake), anchorage-independent growth (e.g., soft
agar
assays), and apoptosis (e.g., measured by DNA-laddering induced upon exposure
to
radiation in the presence of the compound). This preliminary evaluation will
further
define the GEF that predicts activity in tumor suppression (as measured by the
in vitro
surrogate assays). The in vitro systems can be also used to evaluate efficacy
of
combinations of "hits" that may synergize to generate a GEF that predicts
tumor
suppressor function.
E. In vivo evaluation
Representative compounds are selected for in vivo evaluation based upon
their activity in in vitro bioassays, potency in the GEF assays, and other
available
information. The efficacy of compounds in suppressing the growth of human
tumor
xenografts in nude, athymic mice will be assessed as a measure of tumor-
suppressive
activity. Positive controls for this study are the same tumor cells which are
engineered
to express an inducible p53 protein, which enables regulation of tumor growth
in vivo.
F. GEF definition and lead compound optimization
The GEF profile that correlates with in vitro and in vivo efficacy can be
used directly as a means of optimizing "lead" compounds. This is a preferred
step for
any combinations of compounds that are active in the in vitro bioassays, since
the
combination therapy may be difficult to evaluate in in vivo assays due to
possible
pharmacokinetic differences of the components of the mixture. At this stage of
analysis,
ECSOs and maximal responses for the derivative compounds for each assay are
considered.
Depending upon the selectivity requirements for the desired drug, it may
be useful to incorporate additional assays into the screening panel at this
stage. In that
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case, additional in vivo evaluation is necessary to validate the
predictability of the new
GEF for in vivo efficacy and selectivity.
IV. Identification of Agents that Block Cell Invasion for Cancer Thera_py
5 Therapeutic agents that prevent the progression of primary cancer to the
metastatic stage are important members of the arsenal of anti-cancer drugs.
Different
aspects of the process by which a cancer cell enters the bloodstream, leaves
it, and
re-establishes itself at a distant site are potential targets for anti-
metastatic drugs.
However, there is a paucity of in vitro and in vivo models that predict the
10 metastasis-forming ability of human cancer cells; this makes the
identification of
anti-metastatic agents particularly challenging.
A critical aspect in this progression is the process by which cells pass
through the endothelial lining of the blood vessel and invade into the
surrounding stroma.
Cell invasion through a reconstituted basement membrane (e.g. Matrigel) can be
15 employed as an in vitro surrogate for the in vivo event. The assay,
however, is not
readily adaptable to the screening of large compound libraries. The GEF
methodology
can be used to develop a screen for agents that block or decrease cell
invasion and/or
metastasis.
Rather than employing a reference compound for identification of gene
20 expression differences, the genes for this screen are identified by
comparing reference
states. Exemplary reference states may include, but are not limited to the
following:
invasive vs. non-invasive cell lines, normal vs. invasive carcinoma tissue, or
two
histopathologically-staged malignant tissues (e.g., prostatic carcinomas of
Gleason Grades
TII and IV).
A. Cells and Genes
Both cells and tissue specimens which represent various stages in cancer
progression (e. g. from normal to highly invasive or metastatic) are used as
sources of
RNA. An exemplary set of cell lines or strains for studies of breast cancer
progression
is based, for example, on reported in vitro invasive properties (e. g. ,
normal human
mammary epithelial cells, immortal MCF10A or 184B5, poorly invasive MCF7, ZR75-
1,
MDA4b8, moderately invasive MDA435, and highly invasive MDA231 or BT549
(available from ATCC, Rockville, MD). Tissue samples can include human
xenografts
from immunodeficient animals, biopsies that have been dissected by a
pathologist to
CA 02317650 2000-07-06
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36
specifically include tumor, normal, and invasive material or similarly
characterized cells
generated, for example, by Laser Capture Microdissection (Emmert-Buck et al. ,
Science
274: 998-1001 (1996)). Although there is scientific rationale for the
comparison to be
made amongst cells and biopsy specimens derived from the same tissue of
origin, this is
not required because a process common to the metastasis of different cancer
types could
be targeted by deriving a screen using cells and biopsies from other tissues.
Several approaches can be taken to determine the gene expression
differences and similarities among these RNA samples. The RNA isolated from
the
normal and the most invasive cells (or biopsies) can be compared using methods
described above for identifying differences between treated and untreated
cells (e. g.
DD-PCR, subtractive cDNA libraries, high density cDNA arrays). Pooled samples
from
normal vs. tumor cell lines or specimens representing different stages of
cancer
progression may also be used to generate this gene expression comparison and
are, in
fact, preferred because of the greater pool of differentially expressed
sequences that is
likely to be generated. This is particularly important with regard to the
tumor cells,
since it is known that there is individual variability in tumors; these
differences are likely
to be reflected in different gene expression profiles.
The genes that are differentially expressed between normal and highly
invasive cells are selected for further evaluation.
B. Assay characterization and selection
Genes identified as differentially expressed in the first step are assessed
for inclusion in the GEF based upon their expression in the cells being
considered for use
in the screening process. For example, if the initial gene identification was
carried out
using RNA isolated from tissue specimens and not cell culture material, some
genes
expressed in vivo may not be similarly expressed or regulated in the culture
environment.
Preferably cell lines which express the greatest number and the highest levels
of mRNA
for the differentially expressed genes would be chosen for the GEF assays.
In the process of evaluating the expression of the candidate genes in
normal vs. invasive cultured cells, it is also desirable to test their
relative expression in
tumor cells that are either not invasive or poorly invasive. By comparing the
gene
expression patterns in these cells, a subset of the genes can be identified
that is
commonly modulated in only invasive cells or in the majority of the invasive
cell lines
tested. This subset will be especially informative for inclusion in the GEF.
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In some embodiments, regulation of expression of any of the candidate
genes by agents that are reported to modulate cancer cell invasion (e. g.
TGFB, metastasis
suppressor nm23, anti-Ha-ras ribozymes) is determined. The genes whose
expression is
affected by these agents are then included in the GEF.
The "best" assays (e. g. gene/ cell combination with greatest fold response
and signal-to-noise ratio for detection) are chosen for the compound screen.
Appropriate
genes to be used as indicators of cytotoxicity (e.g. gadd45, hsp 70) or as
internal
controls (e.g., GAPDH) are also incorporated into the GEF.
C. Compound screening
Evaluation of gene expression patterns elicited by compounds is similar to
other searches described above. "Hits" are grouped according to their GEF and
re-tested
to determine ECSO for activity in each assay.
D. Preliminary cell-based assays
The "hits" are initially tested in in vitro assays for invasion (e.g. modified
Boyden chamber (Albini et al., Cancer Res. 47:3239-3245 (1987)). This
preliminary
evaluation further defines the GEF that predicts activity in tumor cell
invasion (as
measured by the in vitro surrogate assays). The in vitro systems can also be
used to
evaluate efficacy of combinations of "hits" with different GEF that may
demonstrate
activity when mixed together but not when tested alone.
E. In vivo evaluation
Representative compounds are preferably selected for in vivo evaluation
based upon their potency in the GEF assays. The efficacy of compounds in
suppressing
tumor invasion can be assessed by a number of methods, including metastatic
growth of
human tumor xenografts in nude, athymic mice or the invasion of tumor cells
implanted
on the renal capsule.
F. GEF definition and lead compound optimization
The GEF profile that correlates with in vitro and in vivo efficacy can be
used directly as a means of optimizing "lead" compounds. This will be an
essential step
for any combinations of compounds that are active in the in vitro bioassays,
since the
combination therapy will be difficult to evaluate in in vivo assays due to
probable
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38
pharmacokinetic differences of the components of the mixture. At this stage of
analysis,
ECSOs and maximal responses for the derivative compounds for each assay are
considered.
Depending upon the selectivity requirements for the desired drug, it may
be useful to incorporate additional assays into the screening panel at this
stage. In that
case, additional in vivo evaluation is necessary to validate the
predictability of the new
GEF for in vivo efficacy and selectivity.
V. Identification of Agents that Prevent or Inhibit Breast Tumor Pro ression
A. Back,round
The progression of breast cancer (BC) from a hormone-dependent, well-
differentiated carcinoma to a more advanced stage lesion is marked by the loss
of
estrogen receptor (ER) function, decreased estrogen-cadherin (E-cadherin)
expression or
function, and increased vimentin expression. This progression resembles the
epithelial-
mesenchymal transition (EMT) (Hay, Acta Anat. 154:8-20 (1995)) that occurs
during
embryonic development. The advanced stage breast cancer cells adopt structural
and
functional characteristics of mesenchymal cells. Altered expression of
intermediate
filament proteins contribute to this phenotype (e. g. , decreased expression
relative to less
advanced cancer cells of some keratins and the induction of vimentin
synthesis).
Additional changes include the decreased expression/function of cell
functional
communication proteins (e.g., E-cadherin, ZO-1), attachment factors (e.g.,
integrins),
and extracellular matrix proteins (e.g., thrombospondin) as well as increased
proteolytic
activity (e.g., stromelysin, MMPs). A significant proportion of late stage,
advanced
breast cancers (ABC) are represented in vitro by cultured BC cells that
exhibit hormonal
independence, decreased intercellular communication and adhesion, enhanced
motility,
and increased invasiveness through a reconstituted basement membrane (i. e. ,
matrigel)
(Thompson et al. , J. Cell Physiol. 150:534-544 ( 1992)).
Since motile and invasive abilities are the primary distinguishing
characteristics of ABC cells, we have designed experimentation to identify
Gene
Expression Fingerprints (GEFs) that can be substituted for the phenotypic
assays
generally used to measure these activities. Additional GEFs can be designed to
substitute
for other assays typically used to measure cancer cell progression, such as
proliferation
(e.g., proliferative activity), apoptosis (e.g., apoptotic response),
angiogenesis (e.g.,
angiogenic activity), differentiation, inflammation, and cell-cell or cell-
matrix interaction.
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39
The strategy is to identify genes whose expression is changed in the majority
of ABCs
and is also modulated during the process of tumorigenesis or tumor/metastasis
suppression. Genes in the set of common differentially expressed genes whose
expression is altered by known anti-invasive or anti-metastatic drugs will be
preferentially included in a GEF used for drug screening. The GEFs will be
diagnostic
for ABC and predictive of drug efficacy in the treatment of ABC. The
alteration of the
GEF of the screening cell lines) identifies a compound as a potential lead for
further
optimization.
B. Developing Dia,~nostic GEFs for Weaker and Highly Invasive
Breast Cancer
In order to derive a GEF that can be employed in compound screens for
agents that prevent progression to or inhibit the invasive and/or metastatic
activity of
breast tumors, we began by identifying gene expression changes that are
commonly
found in BC cell lines relative to normal cells. For these studies, we
analyzed fourteen
established cell lines derived from clinical specimens cultured from primary
or metastatic
samples obtained from patients diagnosed with infiltrating ductal carcinoma,
which is the
most prevalent type of breast cancer (Table 5, Groups I-IiI). Many of these
cell lines
have been extensively characterized for their in vitro growth characteristics
and invasive
ability as well as their in vivo tumorigenic and metastatic capacity.
Expression of the
informative marker genes ER, E-cadherin, and vimentin separates the BC cell
lines into
three groups [Table 5: group I is ER-positive (ER+), E-cadherin positive (E-
cad+),
vimentin-negative (Vim-); group II is negative for all markers; group III is
negative for
ER and E-cadherin expression, but positive for vimentin expression]. When
categorized
based upon their invasive ability in the Boyden chamber assay, these BC cell
lines are
separated into only two groups: a weakly invasive (Inv-w) one (encompassing
cell lines
in groups I and II) and a highly invasive (Inv-h) one (group III). It is
noteworthy that all
of the BC cell lines that express vimentin are highly invasive and exhibit a
characteristic
stellate morphology when cultured in matrigel. In vivo, the cells in this
group are the
only BC cell lines that are capable of forming metastases to either the lung
and lymph
nodes (i. e. , MDA231, Hs578T, MDA435) or the brain (i. e. , MDA435) (Price et
al. ,
Cancer Res . 50: 717-721 ( 1990)] .
CA 02317650 2000-07-06
WO 99/37817 PGT/fJS99/01552
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41
The gene expression profiles for all of the BC cell lines that represent
different clinical stages and phenotypic states in BC progression have been
determined by
using cDNA arrays obtained from Clontech (i. e. , Human Atlas I) . This
analysis can be
expanded to include additional genes (e.g., other arrays, cDNA libraries) and
cell
sources. As a reference for these studies, we analyzed the gene expression
patterns in
MCF10A, a spontaneously immortalized "normal" mammary epithelial cell (MEC)
line
derived from a patient with fibrocystic breast disease (Soule et al. , Cancer
Res. 50:6075-
6086 (I990)). The gene expression profiles of additional "normal" cell
cultures (i.e.,
76N MEC strain (Band and Sager, Proc. Natl. Acad. Sci. U.S.A. 86:1249-1253
(1989)
and 184B5 benzopyrene-immortalized MEC (Stampfer and Bartley, Proc. Natl.
Acad.
Sci. U.S.A. 82:2394-2398 (1985)) derived from reduction mammoplasty specimens
were
also obtained. RNA from each of the cell lines was isolated and used to
prepare a
radiolabeled complex cDNA probe for hybridization to the Atlas I arrays. These
filters
contain cDNA fragments corresponding to 588 different genes that represent six
functional gene classes, including oncogenes and tumor suppressor genes, genes
involved
in cell cycle control, cell-cell interactions, apoptosis, and signal
transduction pathways.
Approximately 300 of the 588 genes were detectable in these analyses
indicating that
over half of the genes present on the Atlas I array are expressed in human
mammary
epithelial cells. The hybridization signals from each cDNA spot were
quantitated and
compared with the signals obtained for the same gene in the arrays hybridized
with a
probe prepared from the reference MCFIOA RNA.
An important component of the development of a GEF for compound
screening is the identification of gene expression changes that can be used to
discriminate
between tumor-derived and "normal" cells as well as highly invasive and weakly
invasive
25~ tumors. This is particularly critical in developing strategies to screen
for anti-cancer
drugs because cancer is the result of genomic instability and accumulated
somatic
mutations that lead to complex changes in gene expression. We therefore
searched for
genes whose expression was found to be commonly altered in tumor vs "normal"
cells or
in a subset of tumor cells (e.g., in the four highly invasive BC cell lines).
Table 6 lists
the genes whose expression was frequently altered in the tumor cells relative
to the
reference "normal" control. The values correspond to the number of cell lines
in which
changes in mRNA level of at least two-fold were observed for the indicated
gene. Out
of the 28 genes listed, 11 were differentially expressed in the majority of
the tumor cell
lines compared to the reference "normal" control (Table 6}. The plectin gene
was
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42
differentially expressed in all 14 BC cell lines, whereas the Levels of the B-
myb,
transferrin R, and ICH-2 protease genes changed in 8 of the 14 cell lines (see
Table 6).
Table 7A shows the fold-differences in mRNA level observed for these
genes in each of the cell Lines relative to its expression in the reference
MCF10A. The
expression of most of the genes (i. e. , 8/ 11) was decreased in the BC cells
relative to
"normal" cells. The other three genes (i. e. , B-myb, MacMarcks, and
transferrin R)
showed elevated expression in the BC cell lines. Other "normal" cells (i. e. ,
76N and
184B5) exhibited minimal alteration in the expression of these genes (Figure
3A and data
not shown) . The pattern of expression changes (i. e. , increases or decreases
relative to
"normal" cells) for these genes represent "tumor-associated" changes found in
cultured
breast tumor cell lines.
We also identified genes whose expression changed primarily in BC cell
lines that were categorized as either weakly or highly invasive. Table 6
delineates the
number of cell lines in either the weakly or highly invasive groups that
showed
differential expression of the indicated genes. Two of the genes (i. e. , GST
P and
integrin A-3) were differentially expressed relative to "normal" in all 10
cell lines that
have poor invasive ability; the c-jun gene was differentially expressed in all
four highly
invasive cell lines. The actual changes in expression level measured for each
of these
genes is tabulated (Table 7B). In contrast to the "tumor-associated" genes
described
above, most of the genes associated with either weakly or highly invasive cell
lines were
over-expressed in those cells relative to the "normal" cells. For the c-jun
gene, a1I of the
highly invasive cell lines express higher mRNA levels than the reference
"normal". For
the GST P gene, all 14 cell lines express less mRNA than the reference, but
the highly
invasive BC cell lines have higher levels of GST P mRNA than the weakly
invasive
lines, as indicated by the smaller negative value changes. These data
demonstrate that
some genes are differentially expressed (or repressed) in the weakly invasive
cell lines.
Other genes are differentially expressed (or repressed) in the more
aggressive, highly
invasive tumor cell lines.
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43
Table 6.
t3ene # o! BC Lines wl Expression Chanae
All Tumors Weakly Irn Highly Inv
B-myb 8
MacMarcks 12
Transferrin 8
R
INTEGRIN A-fi 12
INTEGRIN B-4 13
LOW-AFF NGF 12
R
CDK inh p21 13
GC-Box BP 12
Plectin 14
Alb D-box BP 10
ICH-2 PROTEASE 8
GATA-3 8 0
RASP II 6 0
ERBB-3 5 0
HOX C1 PROT 6 0
G NUC BP G-S 9 2
ID-2 6 0
T08 8 0
iNTEGRiN A~ 10 1
DB1 6' 1
'
GST P 10 ' 4'
Fra-1 5' 4
'
o~un 1 4
bFGF R 0 3
INTEGRIN A- 0 2
N~adherin 0 3
TyrK R axi 0 3
il_-8 0 3
Total Analyzed 14 10 4
The number of cell lines with changes in expression of the indicated gene
relative to MCF1 OA is provided. Only fold-changes greater than 2 were scored.
'direction or degree of expression change is different in weaidy vs. highly
irnrasive caNs
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44
f0 N ~ M1 q ~P ~"~ N N ~ W 't! Cf ~ tn
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t~ m.cv~~qaQN°PYq a E ~ N ~? ~~o~oao r' ~i
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'~'~~qy°r~r~ _~ ~ c~~.~oo~o,~
= 0C W = CC
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xxxxx~~o~o~ x
a ac
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The consensus GEFs for weakly and highly invasive cancers are
graphically depicted in Figure 3A. The GEF of a normal MEC strain (i.e., 76N)
is also
shown for comparison. Three sub-profiles can be distinguished: a tumor-
associated GEF
comprising 11 genes (Figure 3A, left-handed striped bars (bars having a stripe
angling
5 downward from left to right)), a GEF representative of weakly invasive
carcinomas
comprising 8 genes (Figure 3A, solid bars), and a GEF diagnostic for highly
invasive,
ABC comprising 6 genes (Figure 3A, right-handed striped bars {bars having a
stripe
angling upward from left to right)). Three genes show distinguishable
differential
expression patterns in both weakly and highly invasive cell lines relative to
"normal"
10 (Figure 3A, stippled bars) and are therefore diagnostic for either invasive
state. These
data strongly suggest that the expression pattern of the 28 genes in an
uncharacterized
cell line could be used as a means of predicting its tumorigenic and invasive
potential.
We analyzed the GEFs of two cell lines that have not been tested for invasive
activity.
One of these is a cell line derived in our laboratory from a breast
fibroadenoma tissue
15 specimen that was cultured and immortalized by transfection with the HPV
E6/E7
oncogenes. The other is the HBL100 cell line that was established from human
milk
epithelial cells and subsequently shown to contain integrated SV40 genomic
sequences
that encode the T antigen protein (Vanhamme and Szpire, Carcinogenesis 9:653-
655
(1988)). The expression profiles of these two cell lines are shown in Figure
3B. From
20 these patterns, we predict that the HBL-100 cell line is a tumor-derived
mesenchymal-
like, highly invasive cell line; in contrast, the 006FA-2B cells are
significantly different
from "normal" immortal HMEC such as the MCF10A and 184B5, but do not exhibit
the
differential gene expression pattern of either of the tumor cell phenotypes
profiled in
these studies. The growth characteristics in matrigel of these two cell lines
were assayed
25 in order to determine whether they demonstrated the morphology associated
with the
phenotypes predicted by their GEF. In agreement with the GEFs for these cell
lines, the
006FA-2B adopted a fused morphology in matrigel whereas the HBL-100 grew with
the
stellate morphology characteristic of mesenchymal cells with highly invasive
ability (data
not shown).
30 The GEFs identified in cell culture models of breast cancer have value in
staging clinical specimens or evaluating responses to drug therapy. The gene
expression
patterns were determined for three tumor biopsies obtained from patients with
moderately
differentiated infiltrating ductal carcinomas of the breast and compared with
the gene
expression profile of normal breast tissue. In the profiles shown in Figure
3C, the
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46
characteristic tumor-associated GEF is found in all three of the tumors, being
most
pronounced in tumors T8911044 and T8911045. Furthermore, all of these tumors
exhibit a GEF that is correlated with weakly invasive tumors. These data
indicate that
GEFs similar to those described here useful in the diagnosis and treatment of
cancer
S patients. They also suggest that the cultured cells faithfully reproduce
some of the gene
expression changes observed in the in vivo tumor environment.
C. Development of Process-Associated GEFs
The GEFs identified up to this point are diagnostic of the phenotypic
states of highly and weakly invasive cells. These gene expression differences
are
valuable in diagnostic applications. Also of interest is whether gene
expression
differences are able or sufficient to report the activity of anti-invasive or
metastatic
drugs. The selection of a subset of these 28 genes that is most useful in
predicting drug
efficacy is assisted by determining whether any of these genes are associated
with the
process of malignant progression. To that end, we measured gene expression
changes
that occur during cellular transformation as well as tumor and/or metastasis
suppression.
Models for these processes include oncogene-transformed normal HMEC, tumor
suppressor gene-transfected tumor cells, and treatments with anti-neoplastic
drugs or
differentiating agents. These studies can include analysis of gene expression
patterns
following treatment of cells in vivo or in vitro under a variety of
conditions, including,
but not limited to, culture on matrigel, on low attachment tissue culture
plates, or with
other cell types. Knowledge of the gene expression changes that occur during
the
conversion of a weakly or non-invasive BC cell to one with highly invasive
activity by
treatment with growth factors (e.g., EGF, scatter factor) or transfection with
oncogenes
(e. g. , v-ras) are particularly valuable. Additional model systems that
recapitulate the
EMT (e.g., treatment with anti-E-cadherin antibodies) can also be employed to
define the
genes that report the invasive properties of BC cells. Information concerning
gene
expression changes that correlate with the reduction in invasive capacity in
response to
treatment with drugs or invasion-suppressor gene products is also desirable
for deriving
the GEF for compound screening.
Normal limited lifespan HMEC can be immortalized by expression of the
SV40 T antigen, the HPV E6 oncogene, or selected p53 mutant proteins (Band,
Intl. J.
Oncol. 12:499-507 (1998)). Using the Atlas I array, we measured the gene
expression
changes that occurred in HMEC immortalized by infection with mutant p53-
expressing
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47
retrovirus (Gao et al., Cancer Res. 56:3129-3133 (1996)). The expression level
of 13
genes was affected following immortalization with three different p53 mutant
proteins
that act as dominant-negative inhibitors of p53 function; notably, 6 of them
are included
in the "tumor-associated" GEF (Table 8). These data suggest that inactivation
of p53 is
a critical determinant of the decreased gene expression observed for those
genes. These
data also imply that these genes are reporters of a critical step in the
process of
tumorigenesis - that of cellular immortalization. They also infer that p53
inactivation is
important in the generation of tumors represented by many of these BC cell
lines.
Mutation of p53 is an event that is associated with the majority of breast
carcinomas. It
is of interest that the tumor biopsies also showed decreased expression of 4
of these 6
genes relative to normal tissue controls (Table 8). These studies demonstrate
a means of
identifying a GEF that is representative of the process of tumor formation.
The genes
comprising that GEF which are also identified as diagnostic for ABC would be
included
in the gene-cell combinations used in the drug screen.
The identification of genes that predict anti-invasive drug activity is aided
by measuring the gene expression changes resulting from treatment of highly
invasive
cells with anti-invasive or anti-metastatic drugs. By comparing the effects of
anti-
invasive compounds that have different known mechanisms of action, a common
set of
genes whose expression changes report anti-invasive activity can be derived.
Also
important is the determination of the gene expression changes caused by drugs
that are
ineffective in blocking invasion, but have other anti-neoplastic properties
(e.g., pro-
apoptotic, anti-angiogenic, anti-proliferative), as well as compounds that are
modulators
of signaling pathways that do not result in the inhibition of invasion. In the
studies
presented here, we tested taxol, mevastatin, sodium butyrate, retinoic acid
(RA), and
caffeic acid (CA). Taxol's efficacy is reported to be dependent upon its
inhibition of
microtubule formation, while mevastatin inhibits HMG CoA reductase and
indirectly
protein prenylation, thereby leading to cell cycle arrest in the G1 phase.
Sodium
butyrate is a differentiating agent that causes histone acetylation and
transcriptional
activation. RA has anti-proliferative and differentiating effects in some BC
cell lines
(i.e., ER+), but is ineffective in others (i.e., ER-negative). Both taxol and
mevastatin
are capable of blocking the development of the characteristic stellate
mesenchymal cell
morphology of MDA231 cells, while sodium butyrate is not effective (data not
shown).
Taxol has also been shown to prevent invasion of MDA231 in the Boyden chamber
assay
(Sasaki and Passanti, Biotechniques 24:1038-1043 (1998)) and mevastatin
inhibits
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48
mammary tumor metastases in vivo (Alonso et al. , Breast Cancer Res. Treat.
50:83-93
(1998)). The highly invasive MDA231 BC cells were treated with these compounds
under conditions (i. e. , concentration and time) reported to have maximal
effects with
little toxicity. Taxol, mevastatin, and butyrate treatment caused changes of
greater than
two-fold in the expression of approximately 10 % of the expressed Atlas I
array genes
(i. e. , taxol: 27/300; mevastatin: 33/300; butyrate: 39/300), while little
effect was
observed with either RA or CA treatment. The gene expression profiles of each
of these
compounds are readily distinguishable from each other (Figure 4).
Significantly, 12 of
the 28 genes identified as potential reporters of either tumorigenicity or
stage of
invasiveness are modulated by one or more of these drugs. Moreover, the
direction of
the gene expression change elicited by these drugs for 11 of these 12 genes is
towards a
more "normal" or less invasive GEF (Table 8). For example, the expression of 7
genes
that were either repressed or enhanced in the highly invasive MDA231 cancer
cells
relative to "normal" were reversed. The expression changes for four of the
genes (i. e. ,
RABP II, Integrin A-3, DB1, and GST P) are in the direction towards a less
invasive
GEF (e.g., RABP II expression is elevated following drug treatment to levels
that are
higher than the "normal" cells similar to the expression change in weakly
invasive cell
lines). Such data suggest that these genes are reporters of drug activities
that affect
malignant progression, but they do not necessarily identify genes that can be
used to
predict anti-invasive efficacy per se. The subset of genes that is commonly
regulated by
both mevastatin and taxol, but not butyrate (i. e. , GC-Box BP, RABP II, DB
1), is likely
to report anti-invasive effects, since both of these agents are presumed to
have anti-
invasive activity based upon matrigel morphology studies while butyrate does
not.
Evaluation of additional drug treatments that have anti-invasive effects as
well as those
with only anti-proliferative or pro-apoptotic effects enables further fine-
tuning of the
GEF that is most predictive of drug efficacy, selectivity for invasive action,
and potential
toxicity.
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49
Table 8. GENE
EXPRESSION CHANGES
Diagnostic Process
M BC Cell Lines
in Bio~ies Tumo
enesis Anti-cancer
Dru
Gsne Weakl Inv
H h Inv inactiv
Tax Mev Bu
B-myb + +
MacMarcks + +
Tn3nsferrin R + +
INTEGRIN A-8 - - - -
INTEGRIN B-4 - - -
LOW AFF NGF R - - - - +
CDK Inh p21 - - - - +
GC-Box BP - - -
+ +
H pledin - _ _ -
Alb D-box BP - - - +
ICH-2 PROTEASE - -
GATA-3 + +
RABP II + + + ~ +
ERBB~3 + +
HOX C1 +
C
GN BP G-S +
ID-2 + +
ToB + +
INTEGRIN A-3 - -
D81 - - -
a GST P - - - - -
Fra-1 - + - -
o-jun +
bFGF R +
INTEGRIN A- 5 +
N-cadherin +
TyrK R axl + _ - -
IL-8 + + + +
The direction of expression change for each of the indicated genes is
tabulated under the Diagnostic
heading for differences in BC Cell Lines and Tumor Biopsies relative to MCF10A
and normal breast
tissue, respectively (data from Tables 7A and 7B and Fig. 3C). Under the
Process heading, genes
modulated in cells immortalized by p53 inactivation relative to their limited
lifespan counterparts are
indicated in the Tumorigenesis column. The direction of gene expression change
in the highly invasive
MOA231 cells in response to treatment with either taxol (taxol), mevastatin
(mev), or sodium butyrate
(buty) is provided in the Anti-cancer drug column.
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D. Defining a GEF for Anti-Invasive Dru~Screenin_g
The studies described here have derived a GEF incorporating the
expression of 28 genes that is useful in distinguishing between weakly and
highly
invasive BC cell lines and tumor biopsies. Within the GEF there is a subset of
gene
5 expression changes associated with all BC cell lines and tumors (i. e. ,
tumor-associated
GEF). In combination with the tumor-associated GEF, two other distinct sub-
GEFs
define weakly vs. highly invasive cancers. Experiments using tumor progression
model
systems (i. e. , p53 inactivation) and anti-neoplastic drug treatments have
identified genes
within the 28 that are modulated in the process of tumorigenesis or during the
inhibition
10 of invasion.
The precise GEF that predicts anti-invasive drug efficacy is a change in
the expression of a subset of the 28-gene GEF representative of highly
invasive cancer
cells. That subset is determined by a selection procedure similar to the one
used to
derive the diagnostic GEFs. Genes commonly affected by drugs or other agents
which
15 modulate the invasive phenotype are compared with the diagnostic GEF to
derive the
common gene expression changes; this produces a GEF predictive of drug
efficacy. The
gene-cell combinations used to create the screen for anti-invasive compounds
includes the
highly invasive MDA231 cell line and at least two genes from each of the sub-
GEFs
described above (i. e. , tumor-associated, weakly invasive, and highly
invasive). Gene
20 and cell line selection also considers data from drug treatment of the
other highly
invasive cell lines as well as weakly invasive ones. The GEF screen can be
carried out
in more than one cell line either in mixed or parallel cultures.
E. Materials & Methods
25 1. Cell Culture and Compound Treatment
The 76N human MEC strain and the 184B5 benzopyrene-immortalized
human MEC line were cultured in DFCI-1 medium (Band and Sager, Proc. Natl.
Acad.
Sci. U.S.A. 86:1249-1253 (1989)). The 006FA-2B cell line was established from
a
benign fibroadenoma tissue sample by co-transfecting the cultured organoids
with
30 plasmid vectors encoding the HPV 16 E6 and E7 oncogenes and a selectable
SVneo
plasmid using a standard calcium phosphate-mediated procedure. 006FA-2B is one
of
several stable epithelial cell clones with extended lifespan that were
selected using 6418
(100 ug/ml, Gibco). MCF10A, HBL-100, T47D, ZR75-1, MCF7, BT483, MDA361,
BT474, BT20, MDA468, SKBR3, MDA453, BT549, Hs578T, MDA231, and MDA435S
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WO 99/37817 PCTNS99/01552
51
cells were obtained from the ATCC (Rockville, MD) and initially cultured in
the ATCC-
recommended medium. To determine the steady state gene expression profiles of
the
breast tumor lines, the cells were cultured to 80-90 % confluency in a-MEM
medium
[alpha-modii=ied MEM supplemented with 1 mM HEPES, 2 mM glutamine, 0.1 mM
MEM non-essential amino acids, 1.0 mM sodium pyruvate, 50 ~cg/ml gentamicin,
1.0
~cg/ml insulin (all from Gibco, Gaitherburg, MD), and 10 % FBS (Intergen)]. To
evaluate the effect of selected compounds on gene expression in the MDA231
cell line,
cells were plated ( 106/ 100 mm dish) in a-MEM medium and allowed to attach
overnight.
Cells were fed with fresh medium containing 3 mM sodium butyrate (Specialty
Media,
Inc. Lavallette, NJ), S.0 ~.M taxol (Molecular Probes, Inc., Eugene, OR), 10'8
M caffeic
acid, 1.0 M retinoic acid, or 20 ~,M mevastatin (all from Sigma) and cell
monolayers
harvested 72 hours (h) later for RNA isolation.
2. Gene Expression Analysis
Total RNA from cell lines and compound-treated cells was isolated by the
guanidinium-isothiocyanate-CsCI gradient procedure (Chirgwin et al. ,
Biochemistry 18:
5294-5299 (1979)). Total RNA from normal and tumor tissue specimens was
obtained
from BioChain Institute, Inc (San Leandro, CA).
The preparation of radioactively labeled cDNA from total RNA (5 ,ug)
was performed essentially as described in the Clontech Atlas I cDNA array
hybridization
kit protocol. The only exceptions were the step for removal of unincorporated
nucleotide
triphosphate, which was carried out using a G50 spin column and the length of
prehybridization, which was increased to at least 6 h. The probe concentration
routinely
employed in the hybridization reactions was 0.7-1.0 x 106 counts per
minute/milliliter
(cpm/ml) .
3. Ima eg_ Analysis of Clontech Atlas I cDNA ExRression Arrays
The probe intensities at each target (cDNA) spot on the Atlas I arrays
were quantitated using the "Array Vision" software package from Imaging
Research, Inc.
(St. Catherine, Ontario, Canada). The grid definition protocol was used in
this analysis
with an automated algorithm to finely adjust the grid to overlay the targets.
Each target
in the array was scanned using the Storm Phosphorimaging System by Molecular
Dynamics. Inc. (Sunnyvale, CA) and a data table was constructed of the average
PSL x
area values (the PSL value per pixel times the area in mm of the target)
corrected for
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WO 99/37817 PCT/US99/01552
52
background and reference normalization. An average background was determined
from a
selected blank region of the array and a reference value for normalization was
generated
using the average of the signals of alI of the targets on the array. The
ratios and z-score
differences between two samples are calculated and differentially expressed
genes are
identified from a common set of thresholded ratios and differences. For these
analyses,
ratio thresholds were 2-fold and z score values were 0.3.
All references cited herein are expressly incorporated by reference in
their entirety for all purposes.
Although the foregoing invention has been described in some detail by
way of illustration and example for purposes of clarity of understanding, it
will be
obvious that certain changes and modification may be practiced within the
scope of the
appended claims.
