Development of Resistance to Targeted Therapies Transforms the Clinically Associated Molecular Profile Subtype of Breast Tumor Xenografts (original) (raw)

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Experimental Therapeutics, Molecular Targets, and Chemical Biology| September 14 2008

Chad J. Creighton;

1The Dan L. Duncan Cancer Center,

3Medicine and

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Suleiman Massarweh;

5The Department of Internal Medicine and

6Markey Cancer Center, University of Kentucky, Lexington, Kentucky

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Shixia Huang;

1The Dan L. Duncan Cancer Center,

4Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas; and

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Anna Tsimelzon;

1The Dan L. Duncan Cancer Center,

2Lester and Sue Smith Breast Center, and the Departments of

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Susan G. Hilsenbeck;

1The Dan L. Duncan Cancer Center,

2Lester and Sue Smith Breast Center, and the Departments of

3Medicine and

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C. Kent Osborne;

1The Dan L. Duncan Cancer Center,

2Lester and Sue Smith Breast Center, and the Departments of

3Medicine and

4Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas; and

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Jiang Shou;

1The Dan L. Duncan Cancer Center,

2Lester and Sue Smith Breast Center, and the Departments of

3Medicine and

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Luca Malorni;

1The Dan L. Duncan Cancer Center,

2Lester and Sue Smith Breast Center, and the Departments of

3Medicine and

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Rachel Schiff

1The Dan L. Duncan Cancer Center,

2Lester and Sue Smith Breast Center, and the Departments of

3Medicine and

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Requests for reprints: Rachel Schiff, Breast Center, Baylor College of Medicine, Room N1230.02, One Baylor Plaza-BCM 600, Houston, TX 77030. E-mail: rschiff@bcm.tmc.edu.

Received: April 14 2008

Revision Received: June 23 2008

Accepted: June 29 2008

Online ISSN: 1538-7445

Print ISSN: 0008-5472

©2008 American Association for Cancer Research.

2008

Cancer Res (2008) 68 (18): 7493–7501.

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  • Version of Record September 14 2008

Abstract

The effectiveness of therapies targeting specific pathways in breast cancer, such as the estrogen receptor or HER2, is limited because many tumors manifest resistance, either de novo or acquired, during the course of treatment. To investigate molecular mechanisms of resistance, we used two xenograft models of estrogen receptor–positive (ER+) breast cancer, one with and one without HER2 overexpression (MCF7/HER2-18 and MCF7 wt, respectively). Mice with established tumors were assigned to the following treatment groups: estrogen supplementation (E2), estrogen deprivation (ED), ED plus tamoxifen (Tam), all with or without the epidermal growth factor receptor tyrosine kinase inhibitor gefitinib (G). Another group received ED plus the antiestrogen fulvestrant (MCF7 wt only). Tumors with acquired or de novo resistance to these endocrine therapies were profiled for gene expression and compared with tumors in the E2 control group. One class of genes underexpressed in endocrine-resistant tumors (relative to E2-treated tumors) were estrogen inducible in vitro and associated with ER+ human breast cancers (luminal subtype). Another class of genes overexpressed in tumors with acquired resistance in both models represented transcriptional targets of HER2 signaling and was associated with ER−/HER2+ human cancers (ERBB2+ subtype). A third class of genes overexpressed in MCF7/HER2-18 tumors exhibiting de novo resistance to tamoxifen was associated with ER+ human cancers but not with estrogen-regulated genes. Thus, in response to various endocrine therapy regimens, these xenograft breast tumors shut down classic estrogen signaling and activate alternative pathways such as HER2 that contribute to treatment resistance. Over time, the molecular phenotype of breast cancer can change. [Cancer Res 2008;68(18):7493–501]

Introduction

Great strides have been made in breast cancer treatment using therapies that target specific molecular pathways. In the clinic, two tumor molecular markers are used to help predict therapeutic response in breast cancer: estrogen receptor α (ER) and the growth factor receptor HER2 (erbB-2/neu). Approximately 70% of breast cancers express ER (ER+ tumors) and tend to rely on the classic estrogen signaling pathway for growth (1, 2). Patients with these tumors are usually treated with either antiestrogens or aromatase inhibitors to block ER signaling. In addition, the 20% to 25% of tumors with gene amplification of HER2 (HER2+ tumors) rely on its signaling pathway for growth (3), and HER2+ patients can be successfully treated by agents targeting HER2. Tumors that express neither ER nor HER2 rely on poorly defined alternative pathways and currently have no effective targeted therapy. Of the breast cancer patients treated with targeted therapies, many do not respond, and of those who do, many acquire resistance over time. Although adjuvant therapy studies of the antiestrogen tamoxifen show a 40% to 50% reduction in the odds of disease recurrence and a 30% to 35% reduction in mortality in ER+ breast cancer, still de novo and acquired resistance to ER-targeted therapy remains a major problem (4). In the metastatic setting, 30% to 50% of ER+ tumors initially respond to tamoxifen, but essentially all of these tumors eventually acquire resistance, leading to disease progression and death (4).

In previous studies, we have used breast cancer xenograft model systems to investigate the mechanisms of resistance to targeted therapies in vivo (5–10). In our models, as in the clinical setting outlined above, resistance to treatment can take the form of de novo resistance, where the tumor shows no initial response, or acquired resistance, where the tumor initially responds but eventually escapes with progressive growth. Analysis of individual molecular marker expression have revealed a role for growth factor signaling via epidermal growth factor receptor (EGFR) and HER2 (6, 8) and stress-related pathways associated with p38 and extracellular signal-regulated kinase 1/2 (ERK1/2) mitogen activated protein (MAP) kinases (5, 7) in de novo and acquired resistance to endocrine therapy.

In this present study, we profiled the expression of more than 50,000 mRNA transcripts in xenograft tumors that initially were either ER+/HER2+ or ER+/HER2− and that acquired resistance to various targeted therapy regimens. In addition, we profiled ER+/HER2+ tumors that manifested de novo resistance to tamoxifen. The goal of this study was to define and characterize the global gene expression patterns associated with the resistant phenotypes. Several studies using clinical breast tumors have defined distinct global molecular subtypes associated with markers such as ER and HER2 (11–15). An important aspect of our study was to compare the gene signatures derived from our model system with gene signatures derived from clinical breast cancers that reflect specific molecular subtypes, to assess how our models might represent breast cancer in the clinical setting.

Materials and Methods

Xenograft studies. MCF7 wt and MCF7/HER2-18 xenograft experiments were carried out as previously reported (6, 8, 10, 16–18) and are described in Supplementary Material. Details of cell culture methods and animal experiments were previously described (6, 8, 10, 16–18). Tumors were harvested for molecular studies either after 2 to 3 wk of treatment (sensitive, “S,” or early-growth, “E,” tumors, <400 mm3 in size) or when they reached the size of 1,000 mm3 (resistant, “R,” or late-growth, “L,” tumors). Each tumor analyzed was from a different mouse; tumor tissues were removed from each individual mouse and kept at −190°C for later analyses. Graphs constructed for this article (Fig. 1) represent individual tumors from each of the different treatment groups as indicated.

Affymetrix microarray analysis. We used Affymetrix U133 Plus 2 Genechip arrays to determine gene expression patterns of tumor xenografts. RNA extraction was carried out in BioRobot EZ1 workstation using EZ1 RNA universal tissue kit according to the manufacturer's instructions (Qiagen). cDNA synthesis, cRNA labeling, and array hybridization were done as previously described (19). Arrays were processed and normalized using the dChiP software (20). Array data have been deposited in the public Gene Expression Omnibus database, accessions GSE8139 and GSE8140. Expression data set filtering and transformation are detailed in Supplementary Material.

ANOVA using dChip identified genes that differed in mean gene mRNA levels in any one of the xenograft tumor treatment groups. Two-sample t tests determined significant differences in gene expression between any two specific groups of samples (P values were two sided). Selection of t test P value cutoffs for defining gene groups was guided by a desire to have a sizable number of genes for inter–data set comparisons (where a smaller number of tumor profiles were involved in the t test, a looser criteria such as P < 0.01 versus P < 0.001 might be used). Expression values were visualized as heat maps using the Cluster (21) and Java TreeView (22) software. One-sided Fisher's exact tests and Q1-Q2 analysis (23) were used to determine statistically significant enrichment of subsets of genes. Further details on the microarray analysis are given in Supplementary Material.

Results

In vivo models of endocrine therapy resistance in ER+/HER2− (MCF7 wt) and ER+/HER2+ (MCF7/HER2-18) breast cancer xenografts. To further investigate mechanisms of growth inhibition and hormone treatment resistance, we used tumors from two of our previously defined in vivo xenograft models of ER+ breast cancer: one of MCF7 wild-type cells (MCF7 wt) and one of MCF7 cells stably transfected with overexpressing HER2 (MCF7/HER2-18; refs. 6, 8, 10, 16–18). In previous studies, MCF7 wt and MCF7/HER2-18 cells were each established as xenografts in ovariectomized athymic nude mice in the presence of estrogen. When tumors reached a sufficient size (150–200 mm3), mice were randomly assigned to various treatment groups (as detailed in Supplementary Methods and listed in Table 1). MCF7 wt tumors received continued estrogen supplementation (E2), E2 with the EGFR tyrosine kinase inhibitor gefitinib (E2+G), estrogen deprivation (ED), estrogen deprivation plus the antiestrogen tamoxifen (Tam), ED plus tamoxifen with gefitinib (Tam+G), or ED plus ICI 182,780 (fulvestrant, or Fulv). MCF7/HER2-18 tumors received E2, E2+G, ED, ED+G, tamoxifen, Tam+G, or E2+Tam.

Table 1.

Xenograft tumor treatment groups

*

Harvested at early time points (tumor size 200–400 mm3).

†

Harvested at later time points (tumor size ∼1,000 mm3).

Figure 1 shows a representative individual tumor growth curve from each of the treatment groups in each model (MCF7 wt and HER2-18) followed over time. These data and earlier experiments leading up to them have been presented and described in detail in prior reports (6, 8, 10, 16–18). Whereas MCF7 wt tumors in E2-supplemented mice grew at a steady exponential rate, wild-type tumors in treated mice (the ED, tamoxifen, Tam+G, Fulv groups) initially stopped growing (“sensitive” phase of treatment). However, after several months, the E2-deprived tumors begin to grow rapidly at a rate comparable with that of the E2 control group (“resistant” phase of treatment). The time to treatment resistance varied among the treatment groups, with the Tam+G and Fulv groups taking ∼2 to 3 months longer to acquire resistance than the ED and tamoxifen groups; resistance, however, eventually developed in all treatment groups. In the MCF7/HER2-18 model, the behavior of E2-deprived tumors was similar to that of the MCF7 wt model. However, unlike the MCF7 wt tumors, HER2-18 tumors were growth stimulated by ED+Tam as the mechanism of de novo resistance. HER2-18 tumors were inhibited by tamoxifen combined with gefitinib, which restores the antitumor effect of tamoxifen by blocking the HER2 pathway (6).

Figure 1.

In vivo models of endocrine therapy resistance in breast cancer. Ovariectomized athymic nude mice bearing tumors derived from either MCF7 wt or MCF7/HER2-18 cells were randomly assigned (day 1) to various treatment groups as indicated. E2, continued estrogen supplementation; G, EGFR tyrosine kinase inhibitor gefitinib; ED, estrogen deprivation; Tam, estrogen deprivation plus antiestrogen tamoxifen; Fulv, estrogen deprivation plus fulvestrant; E2+Tam, estrogen plus tamoxifen. Graphs show growth curves of representative individual tumors in the different treatment groups over time for the MCF7 wt and HER2-18 models (representation of results from refs. 8, 10, 16–18).

Figure 1.

In vivo models of endocrine therapy resistance in breast cancer. Ovariectomized athymic nude mice bearing tumors derived from either MCF7 wt or MCF7/HER2-18 cells were randomly assigned (day 1) to various treatment groups as indicated. E2, continued estrogen supplementation; G, EGFR tyrosine kinase inhibitor gefitinib; ED, estrogen deprivation; Tam, estrogen deprivation plus antiestrogen tamoxifen; Fulv, estrogen deprivation plus fulvestrant; E2+Tam, estrogen plus tamoxifen. Graphs show growth curves of representative individual tumors in the different treatment groups over time for the MCF7 wt and HER2-18 models (representation of results from refs. 8, 10, 16–18).

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Gene expression signatures of endocrine therapy resistance in MCF7 wt and MCF7/HER2-18 xenografts. MCF7 wt and MCF7/HER2-18 tumors, which had manifested resistance to various endocrine therapies (tumors in the ED, tamoxifen, and Tam+G groups, as well as tumors in the MCF7 wt Fulv and MCF7/HER2-18 ED+G groups)—along with growing tumors from the E2 treatment groups (E2 alone as well as E2+G and MCF7/HER2-18 E2+Tam)—were profiled for 54,675 mRNA transcripts (list of profiled tumor groups in Table 1). In addition to “resistant” tumors, we profiled MCF7/HER2-18 “sensitive” tumors in which growth was inhibited by ED, ED+G, and Tam+G groups. In addition, MCF7/HER2-18 tumors treated and stimulated by E2, E2+G, and tamoxifen were profiled after 3 weeks of treatment (early tumors) and again after 8 weeks (late tumors), at tumor volumes that closely corresponded to the sensitive and resistant tumors of the other treatment groups. One profile of a HER2-18 ED+G–sensitive tumor was taken, its profile appearing similar to the HER2-18 ED–sensitive group (Fig. 2A). For the HER2-18 E2+G group, gefitinib led to significant yet modest inhibition of growth (Fig. 1), and thus we still referred to these tumors as early (“E”) versus late (“L”; rather than sensitive versus resistant).

Figure 2.

Gene expression signatures of endocrine therapy resistance in xenograft mouse models. A, hierarchical clustering of patterns for genes that showed significant expression (P < 0.001, ANOVA) in any one MCF7/HER2-18 treatment group. Expression patterns represented using color map (yellow, high expression; blue, low expression), each row representing a gene, each column representing a tumor. S, tumors sensitive to estrogen deprivation; R, tumors resistant to estrogen deprivation; E and L, tumors collected at early and late time periods, respectively (for treatment groups not showing initial sensitivity, see Fig. 1). B, using clustering pattern of A, template patterns representing the three major gene clusters were defined, and genes that best fitted each pattern were obtained (groups 1, 2, and 3). C, hierarchical clustering of patterns for genes that showed significant expression (P < 0.01, ANOVA) in any one MCF7 wt treatment group (resistant or late tumors profiled only). D, using the clustering pattern of C, two groups of genes (groups 4 and 5) that best fitted predefined template patterns were obtained.

Figure 2.

Gene expression signatures of endocrine therapy resistance in xenograft mouse models. A, hierarchical clustering of patterns for genes that showed significant expression (P < 0.001, ANOVA) in any one MCF7/HER2-18 treatment group. Expression patterns represented using color map (yellow, high expression; blue, low expression), each row representing a gene, each column representing a tumor. S, tumors sensitive to estrogen deprivation; R, tumors resistant to estrogen deprivation; E and L, tumors collected at early and late time periods, respectively (for treatment groups not showing initial sensitivity, see Fig. 1). B, using clustering pattern of A, template patterns representing the three major gene clusters were defined, and genes that best fitted each pattern were obtained (groups 1, 2, and 3). C, hierarchical clustering of patterns for genes that showed significant expression (P < 0.01, ANOVA) in any one MCF7 wt treatment group (resistant or late tumors profiled only). D, using the clustering pattern of C, two groups of genes (groups 4 and 5) that best fitted predefined template patterns were obtained.

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For the MCF7/HER2-18 profile data, we performed hierarchical clustering (24) of all 6,883 transcripts that differed significantly in mean mRNA levels in any treatment group compared with the other groups (P < 0.001, ANOVA). The clustered expression patterns were visualized as a color map (Fig. 2A). From the overall clustering pattern, we observed three sizable gene groups. Going back to the original data set, we used supervised approaches to define the sets of genes with patterns that were representative of each of the three clustering patterns of Fig. 2A (Fig. 2B; Table 2): (_a_) a set of 525 uniquely identified genes, designated group 1 genes, that were more highly expressed in the E2 treatment groups (both with and without gefitinib) than in both the ED/ED+G resistant groups (_P_ < 0.001, _t_ test) and the Tam/Tam+G late/resistant groups (_P_ < 0.001); (_b_) a set of 474 genes, the group 2 genes, that were higher in the Tam/Tam+G late/resistant groups (_P_ < 0.01), the Tam/Tam+G early/sensitive groups (_P_ < 0.01), and the ED sensitive groups (_P_ < 0.01), all compared with the E2/E2+G groups, but were not higher in the ED/ED+G resistant groups compared with the E2/E2+G groups (_P_ > 0.05); and (c) a set of 720 genes, the group 3 genes, that were higher in both the ED/ED+G resistant groups (P < 0.001) and the Tam/Tam+G late/resistant groups (P < 0.001), compared with the E2/E2+G groups.

Table 2.

Gene sets associated with endocrine therapy resistance in MCF7 xenograft mouse models

We analyzed the MCF7 wt profile data in a similar manner to the MCF7/HER2-18 data. Hierarchical clustering of 1,363 transcripts differing in mean mRNA levels in any treatment group (P < 0.01, ANOVA) uncovered two major gene groups of interest (Fig. 2C; as there were fewer experimental groups in the MCF7 wt data set compared with MCF7/HER2-18, using a higher ANOVA P value cutoff for MCF7 wt seemed appropriate to account for the lower power to detect differences in any one group). From the original data set, we obtained the following genes to represent each clustering pattern (Fig. 2D; Table 2): (a) a set of 310 unique named genes, which we designated the group 4 genes, that were higher in the E2 treatment groups [with and without gefitinib (G)] compared with the ED and Fulv resistant groups combined (P < 0.01, t test) and compared with the Tam/Tam+G resistant groups (P < 0.01); and (b) a set of 483 genes, the “group 5” genes, that were higher in both the ED/Fulv resistant groups (P < 0.01) and the Tam/Tam+G resistant groups (P < 0.01) compared with the E2/E2+G groups. There are very likely other gene expression patterns of interest existing in our profile data that are more subtle and may include fewer genes. In this study, however, we focused on analysis of the five major gene groups identified here, leaving open the opportunity to further mine the data in future studies.

For the set of genes in any one of the five groups (Fig. 2B and D; Table 2; complete list was provided in Supplementary Data File 1), we compared the expression patterns of the MCF7/HER2-18 data set side by side with those of the MCF7 wt data set. We observed high overlap between the MCF7/HER2-18 group 1 and the MCF7 wt group 4 (Fig. 3), both of these gene groups being higher in the respective E2 treatment groups compared with the estrogen-deprived groups. Between the groups 1 and 4 genes, 85 were shared; when we relaxed the criteria for inclusion of genes from one group within the other group (looking for genes included in one of the two groups and that would have been included in the other group had cutoffs of P < 0.05 been used to define it), 215 genes could be considered shared. Similarly, the MCF7/HER2-18 group 3 overlapped highly with the MCF7 wt group 5 (Fig. 3), genes in either of these groups being more highly expressed in their respective estrogen-deprived compared with E2 treatment groups; between groups 3 and 5, 117 genes were shared (303 using relaxed criteria). For the MCF7/HER2-18 group 2 genes, which were overexpressed in the tamoxifen-resistant and ED-sensitive tumor groups but not in the ED-resistant groups, no corresponding patterns were found in the MCF7 wt data; in particular, the group 2 genes were not overexpressed in the MCF7 wt tamoxifen-resistant groups as might have been expected (Fig. 3).

Figure 3.

Side-by-side comparison of the gene signatures of endocrine therapy resistance from MCF7/HER2-18 and MCF7 wt xenograft models. In the MCF7/HER2-18 and MCF7 wt data sets, expression patterns for genes belonging to any one of the five groups defined in Fig. 2C and D are shown as a color map. Also shown are the corresponding expression patterns in a profile data set (25) of three different breast cancer cell lines stimulated with E2 over a time course from 0 to 24 h (gray, data not represented). The order of the genes is the same across all three data sets represented, allowing one to observe where the various MCF7 xenograft gene sets from the two models overlap and what proportion of these genes appear estrogen regulated.

Figure 3.

Side-by-side comparison of the gene signatures of endocrine therapy resistance from MCF7/HER2-18 and MCF7 wt xenograft models. In the MCF7/HER2-18 and MCF7 wt data sets, expression patterns for genes belonging to any one of the five groups defined in Fig. 2C and D are shown as a color map. Also shown are the corresponding expression patterns in a profile data set (25) of three different breast cancer cell lines stimulated with E2 over a time course from 0 to 24 h (gray, data not represented). The order of the genes is the same across all three data sets represented, allowing one to observe where the various MCF7 xenograft gene sets from the two models overlap and what proportion of these genes appear estrogen regulated.

Close modal

ESR1 (ER) and PGR (PR) mRNA were both low in most resistant tumors compared with E2 control (Fig. 3). We have previously reported that ER expression at the protein level is lost in the MCF7/HER2-18 resistant tumors, except ED+Tam alone (8). Furthermore, HER2 mRNA seemed elevated in both MCF7 wt and MCF7/HER-18 resistant tumors compared with E2 controls (Fig. 3), which is consistent with our prior observations of increased HER2 protein expression in patients with tamoxifen resistance (7) and in our experimental model (17). These results indicate that the molecular phenotype of tumors can change with time as they develop treatment resistance, changing from an ER+ to an ER− phenotype. We further explored this idea below through analysis of the whole sets of genes associated with resistance.

Genes underexpressed in endocrine-resistant tumors, both MCF7 wt and MCF7/HER2-18, are estrogen regulated and associated with ER+/PR+ human breast cancers. The MCF7/HER2-18 group 1 genes and the MCF7 wt group 4 genes were both more highly expressed in the respective E2 compared with estrogen-deprived treatment groups. To determine which of these genes might be targets of the classic estrogen signaling pathway, we examined the corresponding expression patterns of the groups 1 and 4 genes in an expression profile data set from a previous study (25) of breast cancer cell cultures stimulated by estradiol (E2) over a time course of 1 to 24 hours (Fig. 3). The groups 1 and 4 genes had high enrichment of genes induced by E2 in vitro (i.e., genes in estrogen clusters B, C, and D as defined in ref. 25). Of the 525 group 1 genes (Fig. 2A), 116, more than 20%, were estrogen induced (significance of overlap P < 1E−35, one-sided Fisher's exact), 63 of these within 4 hours of E2 treatment, suggesting that they represent primary estrogen-regulated genes. Of the 310 group 4 genes (Fig. 2D; Table 2), 74 were estrogen induced (Fisher's exact P < 1E−24), 51 within 4 hours.

We then examined the expression patterns of MCF7/HER2-18 group 1 genes and the MCF7 wt group 4 genes in human breast tumors to ascertain the possible clinical context for our experimentally derived genes. We obtained three independent gene expression profile data sets of human breast tumors: one from van de Vijver and colleagues (26) of 295 tumors (226 of them ER+), one from Wang and colleagues (11) of 286 tumors (209 ER+), and one from Miller and colleagues (13) of 247 tumors (213 ER+). We sought to determine whether any enrichment existed between our xenograft gene sets and relevant expression patterns in the human tumor data sets. Enrichment was assessed using the rigorous Q1-Q2 method (described in detail in refs. 23, 27 and in Supplementary Material), which tested against randomly generated xenograft gene sets or human tumor profile labels.

By Q1-Q2 (results shown in Fig. 4A), the groups 1 and 4 gene sets (highly expressed in the E2 control groups for their respectively models; Table 2) were each significantly enriched in ER+ tumors having high PR mRNA levels (these genes being positively correlated as a group with PR mRNA within the ER+ tumors). These patterns were observed in all three tumor data sets examined (group 1: van de Vijver P < 1E−07, Wang P < 1E−08, Miller P = 0.0005; group 4: van de Vijver P < 1E−11, Wang P < 1E−11, Miller P < 1E−6). Furthermore, groups 1 and 4 genes were significantly enriched in ER+ over ER− human tumors in each of the clinical data sets (group 1: P < 0.00005; group 4: P < 1E−06). We evaluated the set of genes shared between groups 1 and 4 (Fig. 3), as well as the genes specific to group 1 and not group 4 and genes specific to group 4 and not group 1; only the genes that were shared between groups 1 and 4 were consistently enriched in ER+/PR+ tumors.

Figure 4.

Enrichment analysis of xenograft tumor genes within human breast tumor expression profiles. A, Q1-Q2 enrichment patterns of the five xenograft gene sets (Figs. 2 and 3) across three different profile data sets of human breast tumors (11, 13, 26). Four different human breast tumor gene rankings were evaluated: genes with higher average expression in ER+ compared with ER− tumors (_ER+_[_over ER−)_]; genes with high correlation (i.e., similarity) with PR mRNA within the subset of ER+ tumors (ER+_PR); genes correlated with HER2 mRNA expression patterns within ER+ tumors (ER+_HER2); and genes correlated with HER2 mRNA within ER− tumors (“_ER−_HER2_”). B, RNA expression patterns across MCF7 xenograft and clinical breast tumor data sets for the following sets of genes: ER, PR, and HER2 (top panels); genes in any one of the MCF7 xenograft groups 1-5 (Fig. 2) that were also differentially expressed in ER+ versus ER− clinical tumors (middle panels); and genes in MCF7 xenograft groups 1 to 5 that were also correlated or anticorrelated with HER2 mRNA in ER− clinical tumors (bottom panels). Alongside the ER status–associated genes are the corresponding patterns for E2-stimulated breast cell cultures. Alongside the HER2/ER− associated genes are the corresponding patterns for MCF7 cells with MAP kinase activated through various genes (28). The order of the genes is the same across all data sets represented. Patterns of enrichment (where xenograft and human tumor expression patterns share high overlap contributing to enrichment associations) are indicated. C, heat map of the Pearson's correlations (genome-wide) between each xenograft tumor profile and the average expression for each of the four major breast tumor molecular profile subtypes (basal, ERBB2+, luminal A, luminal B) as defined by Hoadley and colleagues (29).

Figure 4.

Enrichment analysis of xenograft tumor genes within human breast tumor expression profiles. A, Q1-Q2 enrichment patterns of the five xenograft gene sets (Figs. 2 and 3) across three different profile data sets of human breast tumors (11, 13, 26). Four different human breast tumor gene rankings were evaluated: genes with higher average expression in ER+ compared with ER− tumors (_ER+_[_over ER−)_]; genes with high correlation (i.e., similarity) with PR mRNA within the subset of ER+ tumors (ER+_PR); genes correlated with HER2 mRNA expression patterns within ER+ tumors (ER+_HER2); and genes correlated with HER2 mRNA within ER− tumors (“_ER−_HER2_”). B, RNA expression patterns across MCF7 xenograft and clinical breast tumor data sets for the following sets of genes: ER, PR, and HER2 (top panels); genes in any one of the MCF7 xenograft groups 1-5 (Fig. 2) that were also differentially expressed in ER+ versus ER− clinical tumors (middle panels); and genes in MCF7 xenograft groups 1 to 5 that were also correlated or anticorrelated with HER2 mRNA in ER− clinical tumors (bottom panels). Alongside the ER status–associated genes are the corresponding patterns for E2-stimulated breast cell cultures. Alongside the HER2/ER− associated genes are the corresponding patterns for MCF7 cells with MAP kinase activated through various genes (28). The order of the genes is the same across all data sets represented. Patterns of enrichment (where xenograft and human tumor expression patterns share high overlap contributing to enrichment associations) are indicated. C, heat map of the Pearson's correlations (genome-wide) between each xenograft tumor profile and the average expression for each of the four major breast tumor molecular profile subtypes (basal, ERBB2+, luminal A, luminal B) as defined by Hoadley and colleagues (29).

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The ER+-associated human tumor enrichment patterns described above were evident when viewing the gene expression patterns involved using color maps (Fig. 4B). Of the 215 genes that were shared between groups 1 and 4, a high overlap of 51 (chance expected 13, P < 1E−17, one-sided Fisher's exact) were also higher in ER+ over ER− human tumors; in contrast, 22 genes were lower in ER+ human tumors, a number closer to the chance expected. Nearly all of the 51 group 1/group 4/ER+ human tumor genes in the profile data set of E2-stimulated breast cancer cell cultures (25) showed induction by E2 in vitro (Fig. 4B). These genes also tended to be correlated with higher PR mRNA levels within the ER+ group of tumors (Fig. 4A; although this is not evident in the Fig. 4B representation, as PR was uniformly higher in ER+ over ER− tumors).

Genes overexpressed in MCF7 wt and MCF7/HER2-18 endocrine-resistant tumors are associated with the HER2 pathway and with ER−/HER2+ human breast cancers. The MCF7/HER2-18 group 3 genes and the MCF7 wt group 5 genes (Table 2) were both more highly expressed in the respective endocrine-resistant compared with the E2 control groups. In the HER2-18 data set, endocrine-sensitive tumors had levels of expression for the group 3 genes that were between those of the E2 controls and the fully resistant tumors (Fig. 3), indicating that these genes were correlated with acquired resistance. When considering the expression profile data set of E2-stimulated breast cancer cell cultures, many of the groups 3 and 5 genes seemed to be targets of estrogen-mediated repression (Figs. 3 and 4B; Supplementary Material).

We examined the expression patterns of the MCF7/HER2-18 group 3 genes and the MCF7 wt group 5 genes in human breast tumors (results shown in Fig. 4A). For the subset of ER− human breast tumors in each of three profile data sets, the groups 3 and 5 genes were enriched within the genes positively correlated with HER2 (ERBB2) mRNA levels (group 3: van de Vijver P = 0.006, Wang P < 1E−05, Miller P = 0.03; group 5: van de Vijver P < 5E−6, Wang P < 1E−7, Miller P < 0.001, Q1-Q2). The subset of ER+ tumors, however, had no similar enrichment patterns with respect to HER2 mRNA (Fig. 4A and B), indicating that many of the xenograft groups 3 and 5 genes are overexpressed in ER−/HER2+ compared with ER−/HER2− tumors but are not overexpressed in ER+/HER2+ compared with ER+/HER2− tumors. The ER−/HER2+–associated human tumor enrichment patterns uncovered by Q1-Q2 analysis (Fig. 4A) were evident when viewing the gene expression patterns involved using color maps (Fig. 4B; Supplementary Material).

The ER−/HER2+ tumors from the van de Vijver, Wang, and Miller data sets were an approximate surrogate for the “ERBB2+” tumor class defined by Sorlie and colleagues (14, 15), and the groups 3 and 5 genes also overlapped with genes defining this class (Supplementary Material). In another recent study (28), gene expression profiles were taken of MCF-7 breast cancer cells with stably overexpressed EGFR or constitutively activated HER2 (erbB-2), Raf, or MAP/ERK kinase. The MCF7/HER2-18 group 3 and MCF7 wt group 5 genes each overlapped significantly with the genes high in the MCF-7/HER2 cell lines (Fig. 4B; Supplementary Material). In summary, we observed that breast tumors converted from one molecular phenotype (ER+ or luminal) to another (ER−/HER2+ or ERBB2+) after treatment.

Genes overexpressed in MCF7/HER2-18 tumors with de novo resistance to tamoxifen are associated with ER+/PR+ human breast cancers but are not estrogen regulated. In the MCF7/HER2-18 data set, the group 2 genes were more highly expressed in both the ED+Tam groups (early and late) and ED-sensitive group compared with the E2-treated groups, but were not more highly expressed in the ED-resistant groups (Figs. 2D and 3). These group 2 patterns were not observed in the MCF7 wt data set (as they were not overexpressed in the wild-type tamoxifen-resistant tumors). Interestingly, the expression of the group 2 genes was attenuated in the Tam+G–treated resistant MCF7/HER2-18 tumors relative to the tamoxifen tumors (Fig. 3). The group 2 genes had no significant number of E2-regulated transcripts (Fig. 3). Furthermore, the group 2 genes were enriched in ER+/PR+ compared with ER+/PR− human breast tumors, as well as in ER+ compared with ER− tumors (Fig. 4A). Of the 474 group 2 genes, 52 were higher in ER+ over ER− human tumors (Fisher's exact P = 0.00001), yet virtually none of these genes were estrogen regulated (Fig. 4B).

MCF7 wt and MCF7/HER2-18 endocrine-resistant xenograft tumor profiles are globally associated with the ERBB2+ human breast tumor molecular profile subtype. Previous studies in gene expression profiling of human breast cancers have identified at least four major tumor subtypes (luminal A, luminal B, basal, ERBB2+) and a normal breast tissue group (15). Although our above analyses associated specific sets of genes overexpressed in endocrine-resistant xenograft tumors with the HER2 pathway (Fig. 4A and B), we went on to do a global comparison between each of our xenograft tumor profiles and the molecular profile subtypes as defined by a previously published human breast tumor data set by Hoadley and colleagues (29). For each gene common to our array platform and the Hoadley platform, we computed the mean centroid of the four major Hoadley tumor subtypes and centered each group average on the centroid. We then took the Pearson's correlation (using all 7,316 genes common to both data sets) between the Hoadley centered averages and the expression values of each xenograft tumor.

The profile-to-subtype correlation analyses (graphically represented in Fig. 4C) showed both an overall trend for the E2-treated and sensitive/early xenograft tumors (both MCF7/HER2-18 and MCF7 wt models) to associate with the luminal subtype (either luminal A or luminal B or both), and an overall trend for the resistant/late tumors to associate with the ERBB2+ group. At the same time, a few individual tumors were found to show associations not consistent with those of their experimental group and tumors could associate with more than one subtype (e.g., luminal A and luminal B). By our using all of the profiled genes, the Hoadley results were not influenced by our previous definitions of the five gene groups (Table 2). Therefore, by alternative analyses approaches, we could observe that the xenograft tumors converted from one molecular phenotype to another after acquiring treatment resistance.

Discussion

In our model system, we examined the behavior of two different breast cancer cell lines in response to therapies targeting the estrogen and HER signaling pathways: MCF7 wt, which is thought to represent the ER+/HER2− clinical subtype, and MCF7/HER2-18, which is thought to represent the ER+/HER2+ subtype. When these cell lines are grown as tumor xenografts in the presence of estrogen, they rely on the classic estrogen/ER signaling pathway for growth. When estrogen supplementation is removed, the tumors from both cell lines shut down classic estrogen signaling, as indicated by the loss of expression of the groups 1 and 4 gene sets, more than 20% of which genes are up-regulated by estrogen. It has been hypothesized that breast cancers with acquired resistance to ED or tamoxifen might somehow reactivate classic estrogen signaling (e.g., through increasing estrogen agonist effects of antiestrogens, by ligand-independent activation of ER via growth factor signaling, or by an activating mutation in ER; refs. 6, 30, 31). However, reactivation of estrogen signaling was not observed in our system as a mechanism of resistance.

To acquire endocrine resistance, our MCF7 wt and MCF7/HER2-18 tumors activate growth factor signaling pathways, including HER2 and EGFR (6–8). By correlating the gene expression patterns from our xenograft models with patterns from clinical breast tumors, we determined that our xenograft tumors with acquired resistance represented at the transcriptome level the ER−/HER2+ clinical breast cancer subtype (also known as the “ERBB2+” molecular subtype; ref. 14). The groups 3 and 5 genes (overexpressed in the endocrine-resistant tumors) each shared extensive overlap with genes high in ER−/HER2+ clinical tumors, these genes also being highly enriched for transcriptional targets of HER2 signaling in vitro. In addition, we recently showed (32) that more specific HER-2 inhibition using a variety of compounds had greater efficacy in inhibiting growth of our HER2-overexpressing xenografts.

In this study, we observed in our model system that acquired resistance to various ER-targeted therapies transformed the clinically associated molecular profile subtype of breast cancer from that of an estrogen-dependent, ER+ tumor to that of a growth factor–dependent, ER−/HER2+ tumor. Resistant MCF7 wt tumors had been previously found to retain ER protein expression (16), although here these tumors appeared ER− at the transcriptome level. This switch from ER+ to ER−/HER2+ perhaps is not surprising in the MCF7/HER2-18 tumors, which overexpress HER2 to begin with. However, our MCF7 wt tumors did not overexpress HER2 and targets of this pathway before being deprived of estrogen (Fig. 3; see also refs. 7, 17). This up-regulation of HER2 as well as EGFR has been observed in other preclinical models of endocrine therapy resistance (33–36). Furthermore, recent findings show that HER2 gene amplification can be acquired as breast cancer progresses in patients receiving endocrine therapy (7, 37, 38).

One popular idea in molecular oncology is to identify molecular markers predicting tumor response to targeted therapies. Breast cancer treatment is a prime example of this idea being put into clinical practice, with patients whose tumors express ER receiving endocrine therapy and patients whose tumors express HER2 receiving anti-HER2 therapy. At the same time, our study here suggests that using gene expression of pretreatment tumors to predict response might be limited to predicting initial response and would not necessarily be predictive of possible mechanisms of acquired resistance. In our model, we could not have predicted HER2 as one mechanism of acquired resistance solely on the basis of the pretreatment tumor data. Our study lends support to a cancer treatment paradigm that targets not only the tumor in its pretreatment state, but also the tumor as it might manifest itself during tumor progression and acquired therapeutic resistance.

Our current clinical practice of treating breast cancer disease recurrence based on tumor characteristics from the original diagnosis may, therefore, not take into account the dynamic nature of cancer progression in response to specific treatments. Repeating tumor biopsies at the time of cancer recurrence may be critical to correctly identify molecular targets for more tailored therapeutic options with the hope that this strategy can improve outcome in patients with breast cancer.

Disclosure of Potential Conflicts of Interest

C.J. Creighton: Consultant, GlaxoSmithKline. S. Massarweh: Commercial research grants, Novartis and Bayer. C.K. Osborne: Commercial research grants, AstraZeneca and GlaxoSmithKline. R. Schiff: Commercial research grants, AstraZeneca and GlaxoSmithKline. The other authors disclosed no potential conflicts of interest.

Acknowledgments

Grant support: Breast cancer Specialized Program of Research Excellence Grant (P50 CA58183) from the National Cancer Institute, P30 Cancer Center support grant from the NIH (P30 CA125123), pilot grant from the Dan L. Duncan Cancer Center at Baylor College of Medicine, and research grant from AstraZeneca.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Gary Chamness for editorial assistance and critical review of the manuscript.

References

1

Clark GM, McGuire WL. Steroid receptors and other prognostic factors in primary breast cancer.

Semin Oncol

1988

;

15

:

20

–5.

2

Allred DC, Brown P, Medina D. The origins of estrogen receptor α-positive and estrogen receptor α-negative human breast cancer.

Breast Cancer Res

2004

;

6

:

240

–5.

3

Slamon DJ, Godolphin W, Jones LA, et al. Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer.

Science

1989

;

244

:

707

–12.

4

Osborne CK. Tamoxifen in the treatment of breast cancer.

N Engl J Med

1998

;

339

:

1609

–18.

5

Schiff R, Reddy P, Ahotupa M, et al. Oxidative stress and AP-1 activity in tamoxifen-resistant breast tumors in vivo.

J Natl Cancer Inst

2000

;

92

:

1926

–34.

6

Shou J, Massarweh S, Osborne CK, et al. Mechanisms of tamoxifen resistance: Increased estrogen receptor-HER2/neu cross-talk in ER/HER2-positive breast cancer.

J Natl Cancer Inst

2004

;

96

:

926

–35.

7

Gutierrez MC, Detre S, Johnston S, et al. Molecular changes in tamoxifen-resistant breast cancer: Relationship between estrogen receptor, HER-2, and p38 mitogen-activated protein kinase.

J Clin Oncol

2005

;

23

:

2469

–76.

8

Massarweh S, Osborne CK, Jiang S, et al. Mechanisms of tumor regression and resistance to estrogen deprivation and fulvestrant in a model of estrogen receptor-positive, HER-2/neu-positive breast cancer.

Cancer Res

2006

;

66

:

8266

–73.

9

Osborne CK, Hobbs K, Clark GM. Effect of estrogens and antiestrogens on growth of human breast cancer cells in athymic nude mice.

Cancer Res

1985

;

45

:

584

–90.

10

Benz CC, Scott GK, Sarup JC, et al. Estrogen-dependent, tamoxifen-resistant tumorigenic growth of MCF-7 cells transfected with HER2/neu.

Breast Cancer Res Treat

1992

;

24

:

85

–95.

11

Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.

Lancet

2005

;

365

:

671

–9.

12

van 't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer.

Nature

2002

;

415

:

530

–6.

13

Miller LD, Smeds J, George J, et al. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival.

Proc Natl Acad Sci U S A

2005

;

102

:

13550

–5.

14

Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.

Proc Natl Acad Sci U S A

2001

;

98

:

10869

–74.

15

Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets.

Proc Natl Acad Sci U S A

2003

;

100

:

8418

–23.

16

Osborne CK, Coronado-Heinsohn EB, Hilsenbeck SG, et al. Comparison of the effects of a pure steroidal antiestrogen with those of tamoxifen in a model of human breast cancer.

J Natl Cancer Inst

1995

;

87

:

746

–50.

17

Massarweh S, Osborne C, Creighton C, et al. Tamoxifen resistance in breast tumors is driven by growth factor receptor signaling with repression of classic estrogen receptor genomic function.

Cancer Res

2008

;

68

:

826

–33.

18

Massarweh S, Osborne K, Heidi W, Schiff R. Estrogen deprivation is crucial for the antitumor effect of fulvestrant and adding the HER inhibitor gefitinib delays acquired resistance in a xenograft model of ER-positive breast cancer. Breast Cancer Res and Treat, Proceedings of the 30th Annual San Antonio Breast Symposium-December, 2007; 2007;106:Abstract 2090, S115.

19

Huang S, Podsypanina K, Chen Y, et al. Wnt-1 is dominant over neu in specifying mammary tumor expression profiles.

Technol Cancer Res Treat

2006

;

5

:

565

–71.

20

Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

Proc Natl Acad Sci U S A

2001

;

98

:

31

–6.

21

Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns.

PNAS

1998

;

95

:

14863

–8.

22

Saldanha AJ. Java Treeview-extensible visualization of microarray data.

Bioinformatics

2004

;

20

:

3246

–8.

23

Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ. Discovering statistically significant pathways in expression profiling studies.

Proc Natl Acad Sci U S A

2005

;

102

:

13544

–9.

24

Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns.

Proc Natl Acad Sci U S A

1998

;

95

:

14863

–8.

25

Creighton CJ, Cordero KE, Larios JM, et al. Genes regulated by estrogen in breast tumor cells in vitro are similarly regulated in vivo in tumor xenografts and human breast tumors.

Genome Biol

2006

;

7

:

R28

.

26

van de Vijver MJ, He YD, van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer.

N Engl J Med

2002

;

347

:

1999

–2009.

27

Creighton CJ. A gene transcription signature of the Akt/mTOR pathway in clinical breast tumors.

Oncogene

2007

;

26

:

4648

–55.

28

Creighton CJ, Hilger AM, Murthy S, Rae JM, Chinnaiyan AM, El-Ashry D. Activation of mitogen-activated protein kinase in estrogen receptor {α}-positive breast cancer cells in vitro induces an in vivo molecular phenotype of estrogen receptor {α}-negative human breast tumors.

Cancer Res

2006

;

66

:

3903

–11.

29

Hoadley K, Weigman V, Fan C, et al. EGFR associated expression profiles vary with breast tumor subtype.

BMC Genomics

2007

;

8

:

258

.

30

Osborne CK, Wakeling A, Nicholson RI. Fulvestrant: an oestrogen receptor antagonist with a novel mechanism of action.

Br J Cancer

2004

;

90Suppl 1

:

S2

–6.

31

Tonetti DA, Jordan VC. The role of estrogen receptor mutations in tamoxifen-stimulated breast cancer.

J Steroid Biochem Mol Biol

1997

;

62

:

119

–28.

32

Arpino G, Gutierrez C, Weiss H, et al. Treatment of human epidermal growth factor receptor 2-overexpressing breast cancer xenografts with multiagent HER-targeted therapy.

J Natl Cancer Inst

2007

;

99

:

694

–705.

33

Nicholson RI, McClelland RA, Gee JM, et al. Epidermal growth factor receptor expression in breast cancer: association with response to endocrine therapy.

Breast Cancer Res Treat

1994

;

29

:

117

–25.

34

Brodie SG, Xu X, Qiao W, Li WM, Cao L, Deng CX. Multiple genetic changes are associated with mammary tumorigenesis in Brca1 conditional knockout mice.

Oncogene

2001

;

20

:

7514

–23.

35

Wolf DM, Jordan VC. Characterization of tamoxifen stimulated MCF-7 tumor variants grown in athymic mice.

Breast Cancer Res Treat

1994

;

31

:

117

–27.

36

Osipo C, Gajdos C, Cheng D, Jordan V. Reversal of tamoxifen resistant breast cancer by low dose estrogen therapy.

J Steroid Biochem Mol Biol

2005

;

93

:

249

–56.

37

Meng S, Tripathy D, Shete S, et al. HER-2 gene amplification can be acquired as breast cancer progresses.

Proc Natl Acad Sci U S A

2004

;

101

:

9393

–8.

38

Lipton A, Leitzel K, Ali SM, et al. Serum HER-2/neu conversion to positive at the time of disease progression in patients with breast carcinoma on hormone therapy.

Cancer

2005

;

104

:

257

–63.

©2008 American Association for Cancer Research.

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