Affiliations of authors: S. G. Hilsenbeck, W. E.Friedrichs, R. Schiff, R. K. Hansen, C. K. Osborne, S. A. W. Fuqua (Departments of Medicine/Oncology), P. O'Connell (Department of Pathology), The University of Texas Health Science Center, San Antonio.
Correspondence to: Suzanne A. W. Fuqua, Ph.D., The University of Texas Health Science Center, Departments of Medicine/Oncology, 7703 Floyd Curl Dr., San Antonio, TX 78248-7884 (e-mail: suzanne_fuqua{at}oncology.uthscsa.edu).
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ABSTRACT |
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INTRODUCTION |
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Tamoxifen is the most frequently prescribed drug for the treatment of breast cancer. Its use in breast cancer treatment has expanded from first-line treatment for advanced metastatic disease (8), to adjuvant therapy after surgery for primary disease (9), and possibly to prevent breast cancer (10). Acquired tamoxifen resistance is a clinically important problem because a majority of patients with breast cancer will be offered tamoxifen at some time during their treatment, and although tamoxifen is initially effective in many patients, resistance eventually develops. Clinical resistance is almost certainly heterogeneous and multifactorial. Changes may be at the level of the target estrogen receptor (11-14), at a postreceptor point in the estrogen-receptor-response pathway (15-18), and/or downstream of the response pathway (19-21). With cDNA array technology (6,22,23), we may be able to discern the potentially complex patterns of gene expression that are involved in the acquisition of resistance.
In this study, we have used principal components analysis as a practical, but statistically valid, approach to simultaneously examine array data from several time points in an in vivo model of acquired resistance. The model simulates the clinical tamoxifen-resistant phenotype by using estrogen receptor-positive MCF-7 breast cancer tumors growing in athymic nude mice (24). We demonstrate that principal components analysis can reliably detect moderately sized alterations in gene expression that we have confirmed by western blot analysis.
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MATERIALS AND METHODS |
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MCF-7 breast cancer cells were injected into the mammary fat pads of athymic nude mice
supplemented with an estrogen pellet as described previously (24) until
tumors grew. The estrogen pellets were removed and the animals were treated with tamoxifen.
Tumor volumes then declined and remained stable for several months. Invariably, however, after
initial growth suppression, the tumors became resistant and growth resumed. Animals were killed
at various times to obtain estrogen-stimulated tumors before tamoxifen treatment,
tamoxifen-sensitive tumors during tamoxifen treatment but before acquired resistance, and
tamoxifen-resistant tumors after tumor growth had resumed. We collected five tumors from each
group. We then prepared total RNA with RNeasy kits (Qiagen Inc., Valencia, CA), and isolated
messenger RNA on Dynabeads (Dyna, Oslo, Norway) according to manufacturer's
instructions. For each group, the RNAs were pooled and used to synthesize 32P-radiolabeled cDNAs for hybridization to the Atlas(TM) human cDNA expression array-1,
according to the manufacturer's instructions (25) with
SuperScriptII reverse transcriptase (Life Technologies, Inc. [Gibco BRL],
Gaithersburg, MD). The CLONTECH Atlas(TM) human cDNA expression array is a
positively charged nylon membrane (8 x 12 cm) that is spotted in duplicate with 200- to
600-base-pair cDNA fragments representing 588 genes and 21 housekeeping genes or control
sequences (25). Genes are arrayed in six quadrants with genes of like
function (i.e., oncogenes, assorted receptors, etc.) grouped together geographically. The
hybridization data were collected with a Molecular Dynamics PhosphoImager(TM) (Molecular
Dyanmics, Sunnyvale, CA). This array was essentially the only one available when these
experiments were done. Although the array does not include the estrogen receptor, it does include
many other genes of potential interest in breast cancer, including two that we have studied
previously, hsp27 and heregulin-. We collected data from three arrays, one array for each
tumor type.
Western Blot Analysis
Pulverized frozen tumors were manually homogenized in 5% sodium dodecyl sulfate. After boiling and microcentrifugation (10 minutes at 10 000 rpm, room temperature), clear supernatants were collected, and the protein concentration was determined by the bicinchoninic acid method (Pierce Chemical Co., Rockford, IL) as previously described (26). Twenty-five micrograms of protein was separated on a denaturing polyacrylamide gel and transferred by electroblotting to nitrocellulose membranes (Schleicher and Schuell, Inc., Keene, NH). The blots were first stained with StainAll dye (Alpha Diagnostic Intl., Inc., San Antonio, TX), to confirm uniform transfer of all samples, and then incubated in blocking solution (5% nonfat dry milk in Tris-HCl buffered saline-Tween [TBST = 50 mM Tris-HCl at pH 7.5, 150 mM NaCl, and 0.05% Tween 20]). After brief washes with TBST, the filters then were reacted with primary antibodies to erk-2 (UBI, Lake Placid, NY) or heat shock transcription factor-1 (HSF-1) (Stressgen, Victoria, Canada) for 1 hour at room temperature followed by extensive washes with TBST. Blots were then incubated with horseradish peroxidase-conjugated secondary antibody (Amersham Life Science Inc., Arlington Heights, IL) for 1 hour, washed with TBST, and developed by the ECL procedure (Amersham Life Science Inc.). The autoradiograms from the western blots were scanned with a densitometer, and the data are presented as the area determined for each individual tumor sample.
Statistical Considerations
In this pilot study, each hybridization (m = three arrays) resulted in expression values for 588 genes and 21 control genes (putative housekeeping genes and negative control genes). The control genes, which were arrayed in a separate row at the bottom of the array and were more difficult to quantitate reliably in replicated experiments using the same RNA (data not shown), were not included in the statistical analyses. Expression of the highest and lowest expressed genes on the array varied by two to three orders of magnitude. Logarithmic transformation of the raw data reduced this range and helped equalize variability. This also means that additive effects on the log scale can be interpreted as fold changes in actual expression.
Because of the expense, limited amounts of RNA, and other considerations, array experiments usually have few replications and invariably have orders of magnitude more variables (genes and expressed sequence tags) than observations (hybridizations). In this study, we switch the roles of variables and observations, treating each tumor type as a variable (m = three arrays) and each expressed gene sequence as an observation (n = 588 genes).
Principal components analysis of mean-centered log-transformed data, based on the variance-covariance matrix (27), was then used to standardize across the three hybridizations and to extract three new axes (components P1, P2, and P3), expressed as linear combinations of the original axes (variables ES [estrogen-stimulated], TS [tamoxifen-sensitive], and TR [tamoxifen-resistant]).
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In principal components analysis, the coefficients (As, Bs, and Cs) are chosen so that the first component (P1) explains the maximal amount of variance in the data. The second component (P2) is perpendicular to the first and explains the maximal residual squared variation, and the third component (P3) is perpendicular to the first two. Meaning was ascribed to the new axes by visual examination of the coefficients. In these array experiments, P1 represents the average level of expression across the tumor types and P2 and P3 represent differences between tumor types. A bivariate analysis that results in two new axes (P1 and P2) was also performed to compare tamoxifen-sensitive gene expression with tamoxifen-resistant gene expression. The coefficents do not always have a nice biologically sensible interpretation, although the higher-order components can still be used to identify outlier genes, regardless of interpretation (see below).
We used P2 (and P3 in the higher-order analysis) to identify outlier genes that might represent true alterations in gene expression. In the bivariate principal components analysis of tamoxifen-sensitive gene expression versus tamoxifen-resistant gene expression, we used a normal approximation to construct a 99% prediction region for component P2 (i.e., 0 ± 2.57*SDr, where SDr = interquartile range/1.35). A robust estimate of the standard deviation (SDr) was used to reduce the variance-inflating effects of outliers (28). Genes outside the region were identified for further study. Analogously, in a trivariate principal components analysis (estrogen-stimulated, tamoxifen-sensitive, and tamoxifen-resistant gene expression), we computed a 99% bivariate normal prediction ellipse (27,29) for components P2 versus P3, and genes outside the ellipse were selected for investigation.
This "robust prediction interval" approach seems justified on the following
basis. Although the distribution of P1 is highly skewed, higher-order components are roughly
symmetric. When there is no differential expression, as in a bivariate analysis of two array
hybridizations using the same pool of RNA, the higher-order components are approximately
normally distributed (data not shown). In experiments comparing different pools of RNA, where
some genes may be differentially expressed, the observed distribution of each higher-order
component (P2, P3, etc.) should be a mixture of central (µ = 0) and noncentral
(µ 0) distributions. By using a robust estimator that focuses on the middle of the
observed distribution, which should represent primarily unaltered genes, we hope to increase
sensitivity to identify truly altered genes. The prediction level (99%), which is analogous
to the specificity of a diagnostic test, was chosen arbitrarily as representing a reasonable balance
between identifying too many spuriously "significant" genes and missing true
alterations. For display purposes, we have back-transformed the data by exponentiation of P2 and
P3 so that the data are shown as approximate fold increases or decreases in expression.
The ability of this methodology to detect true alterations was examined in a small simulation
study. Log-transformed values from a hypothetical bivariate array experiment with 588 genes
were generated to have a common log-normally distributed component for level of expression
[i.e., exp(X) + 8, where X ~ N(µ = 0,
= 0.6)], and independent normally distributed errors [i.e., loge (Control) = exp(X) + 8 + Y and loge
(Experimental) = exp(X) + 8 + Z, where Y,Z ~
N(µ = 0,
= 0.17)].
The distributional parameters were chosen to mimic data seen in our real experiments. A
small percentage of truly altered genes (2% or 4%) were created by shifting the
error distribution for the experimental member of the pair up or down (with 50%
probability) to represent an average 2- or 2.5-fold change from baseline [i.e., loge (Experimental) = exp(X) + 8 + W, where W
~ N(µ = ±0.7, = 0.17)]. The generated
data were then analyzed as described above, and the numbers of truly altered and spuriously
altered genes falling outside the 99% prediction region were tabulated. Each scenario was
replicated 100 times, and the results were summarized over all replications. All analyses were
performed with the SAS program package (Version 6.11, SAS Institute, Cary, NC).
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RESULTS |
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Fig. 1 shows the three bivariate log-log scatter
plots that arise from pairwise comparisons of the data from the three
cDNA array hybridizations (one for estrogen-stimulated tumors, one for
tamoxifen-sensitive tumors, and one for tamoxifen-resistant tumors).
Each gene of the 588 genes on the array (excluding housekeeping and
control genes) is represented by a point on the scatter plots. The
individual values ranged over two to three orders of magnitude,
indicating that the most highly expressed genes were expressed at 100-
or 1000-fold higher levels than the lowest expressed genes. For
example, the 27-kd heat shock protein (hsp27) was the most highly
expressed gene on the array in all three tumor types. This finding is
consistent with our previously published result that hsp27 is amplified
and overexpressed in the late-passage MCF-7 cells used in this model
(30). Similarly, the array results are consistent with
previous findings (31) that heregulin-
is expressed at
relatively low levels in all three types of tumor cells.
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Principal components analysis of the log-transformed expression data was used to produce a
new set of axes (Fig. 2). For tamoxifen-sensitive tumors versus
tamoxifen-resistant tumors (Fig. 2
, A), the new x axis or first
principal component (P1) roughly corresponds to the line of "identity" and
represents level of expression. The second principal component (P2) is perpendicular to the first
and represents difference in expression between tumor types. In the bivariate analysis, more than
97% of the total variation in the log-transformed data was associated with P1, leaving
about 3% for P2. The two components are, by definition, not correlated (
=
0). The distribution of P1 is skewed, because many genes on the array are expressed at low to
moderate levels, but only a few are expressed at extremely high levels. The distribution of P2 is
roughly symmetric, and a 99% robust prediction interval identified 35 outlier genes that
may be over- or under-expressed in tamoxifen-resistant tumors relative to tamoxifen-sensitive
tumors (Fig. 2
, B).
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Bivariate principal components analysis could be performed for each
pair of tumor types; however, a more comprehensive three-way analysis
is preferred and is more biologically relevant. Principal components
analysis of the mean-centered log-transformed data (for
estrogen-stimulated tumors, tamoxifen-sensitive tumors, and
tamoxifen-resistant tumors) yields three new axes (P1, P2, and P3) that
account for 90.5%, 8%, and 1.5% of the variation in the
data, respectively. By inspection of the coefficients, the first
principal component (P1) is again interpreted as the "average level
of expression" because the coefficients were all positive and similar
in value (0.63, 0.55, and 0.55, respectively). The second principal
component (P2) clearly contrasts data from estrogen-stimulated tumors
to the average of tamoxifen-sensitive and tamoxifen-resistant tumors
because the P2 coefficient for the estrogen-stimulated data is negative
(-0.78) and roughly equal to the sum of the tamoxifen-sensitive and
tamoxifen-resistant coefficients (0.46 and 0.43, respectively). The
third principal component (P3) primarily represents differences between
the tamoxifen-sensitive and the tamoxifen-resistant tumors, because the
P3 coefficient for the estrogen-stimulated tumors is small (0.02) and
the tamoxifen-sensitive and tamoxifen-resistant coefficients are nearly
equal but opposite in sign (0.69 and -0.72, respectively). Fig.
3 shows a scatter plot of P2 versus P3. Points near
the center represent genes that were similarly expressed in all three
tumor types, whereas points on the periphery exhibit alterations in
expression. Data have been back-transformed to show the approximate
fold changes in expression. We used a bivariate normal approximation
with robust estimates of standard deviations to compute a 99%
prediction ellipse. Genes lying outside the region may exhibit real
alterations in the level of expression that are associated with the
biologic effects during the transition from estrogen-stimulated to tamoxifen-sensitive status and
tamoxifen-sensitive to tamoxifen-resistant status.
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Confirmation of Gene Expression by Western Blot Analysis
We selected two genes just outside of the 99% prediction ellipse
(erk-2 and HSF-1) for quantitation by western blot analysis. These two
genes were chosen because of their relatively low expression (Fig. 1)
and modest alteration, so that we could address sensitivity questions
and the ready availability of specific antibodies. The erk-2 kinase is
a known mediator of the growth factor signaling pathway, and it has
been shown that the estrogen receptor can activate its activity in
MCF-7 cells (32). HSF-1 is involved in cellular stress
responses (33) and is thus a potential marker of
tamoxifen-induced stress. We found that the relative levels of erk-2
and HSF-1 predicted in the array experiment were indeed confirmed in an
independent set of individual tumors (Fig. 3
, B, lanes 1-15) from the
athymic nude mouse model. As predicted by Figs. 1
, A, and 3
, A, western
blot results for HSF-1 indicate a substantial increase in expression in
tamoxifen-sensitive tumors relative to estrogen-stimulated tumors,
which is followed by a decrease in tamoxifen-resistant tumors to
approximately the levels in estrogen-stimulated tumors (Fig. 1
, B).
Similarly for erk-2, there is an increase in expression in
tamoxifen-sensitive tumors relative to estrogen-stimulated tumors (Fig.
1
, A), but there is relatively less change between tamoxifen-sensitive
and tamoxifen-resistant tumors.
Power Considerations
Using distributional parameters from some of our pilot studies, we
ran a series of simulations to investigate the likely sensitivity of
these methods to detect real differences of moderate size (Table
1). With modest changes (twofold) in 2%-4%
of genes, 99% of the unchanged genes were correctly classified as
unchanged by the 99% prediction interval, and 59% of the
altered genes were correctly identified as outliers. With larger
differences (e.g., 2.5-fold), the proportion of correctly identified
outliers goes up (85%). Although the outliers will always be
contaminated by a few spuriously identified genes, these results
suggest that the method has reasonable power to detect real differences.
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DISCUSSION |
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In this study, we used an in vivo athymic mouse model of acquired tamoxifen resistance (24) to explore the power of microarray expression profiling. In this tamoxifen-resistance model, we have previously shown that one potential resistance mechanism is stimulation of the tumor by tamoxifen, which acts as a partial agonist. As our first analysis, we used the array technology to identify those genes that might be associated with this growth stimulation. We hypothesized that the tamoxifen-stimulated phenotype could result from the deregulated expression of downstream growth-regulatory pathways that liberate the cell cycle from normal steroid control. Indeed, it has been reported that overexpression of single growth regulatory genes such as cyclin D1 (34), protein kinase A (35), and transforming growth factor ß (21) can influence a cell's response to tamoxifen treatment. However, there are probably multiple mechanisms that coexist in tumors and in conjunction contribute to the clinical tamoxifen-resistant phenotype. The microarray expression profiling technology is well-suited for this clinical problem. Principal components analysis of our preliminary data suggests that distinct patterns of temporal alteration in gene expression can be distinguished. Our future studies will be aimed at identifying which of the outlier genes are most contributory to the tamoxifen-stimulated phenotype and testing these genes in clinical samples on custom microarrays. From these studies, we expect to identify the gene expression patterns predictive of tamoxifen-resistant growth.
In summary, principal components analysis of log-transformed array data provides a practical approach to data reduction, visualization, and identification of "significant" outlier genes. As a result, analysis of cDNA expression arrays can identify genes and pathways that are altered during the process of resistance. We predict that principal components analysis or related methods of analysis of microarray expression data will lead to the identification of novel growth pathways that are important for the generation of tamoxifen resistance and thus will generate new predictive clinical paradigms.
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NOTES |
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We thank Julia Perkins for preparation of the manuscript.
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Manuscript received August 3, 1998; revised December 18, 1998; accepted December 30, 1998.
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