Development of resistance to targeted therapies transforms the clinically associated molecular profile subtype of breast tumor xenografts

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.

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 anti-estrogen 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 (, , –18)).

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, L, tumors collected at early and late time periods, respectively (for treatment groups not showing initial sensitivity, see Figure 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 clustering pattern of (C), two groups of genes (groups 4 and 5) that best fitted pre-defined template patterns were obtained.

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 datasets, expression patterns for genes belonging to any one of the five groups defined in Figure 2C and 2D are shown as a color map. Also shown are the corresponding expression patterns in a profile dataset (25) of three different breast cancer cell lines stimulated with E2 over a time course from 0–24 hours (gray: data not represented). The order of the genes is the same across all three datasets 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 4

Enrichment analysis of xenograft tumor genes within human breast tumor expression profiles. (A) Q1–Q2 enrichment patterns of the five xenograft gene sets (Figures 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: (1) genes with higher average expression in ER+ compared with ER- tumors (“ER+ (over ER-)”), (2) genes with high correlation (i.e. similarity) with PR mRNA within the subset of ER+ tumors (“ER+_PR”), (3) genes correlated with HER2 mRNA expression patterns within ER+ tumors (“ER+_HER2”), (4) genes correlated with HER2 mRNA within ER- tumors (“ER-_HER2”). (B) RNA expression patterns across MCF7 xenograft and clinical breast tumor datasets for the following sets of genes: (1) ER, PR, and HER2 (top panels); (2) genes in any one of the MCF7 xenograft groups 1–5 (Figure 2) that were also differentially expressed in ER+ versus ER- clinical tumors (middle panels); (3) genes in MCF7 xenograft groups 1–5 that were also correlated or anti-correlated with HER2 mRNA in ER-negative clinical tumors (bottom panels). Alongside the ER-status-associated genes are the corresponding patterns for E2-stimulated breast cell cultures. Alongside the HER2/ER-negative-associated genes are the corresponding patterns for MCF7 cells with MAPK-activated through various genes (28). The order of the genes is the same across all datasets 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 xengraft 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 et al. (29).