Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes

Abstract

The epithelial-to-mesenchymal transition (EMT) produces cancer cells that are invasive, migratory, and exhibit stem cell characteristics, hallmarks of cells that have the potential to generate metastases. Inducers of the EMT include several transcription factors (TFs), such as Goosecoid, Snail, and Twist, as well as the secreted TGF-beta1. Each of these factors is capable, on its own, of inducing an EMT in the human mammary epithelial (HMLE) cell line. However, the interactions between these regulators are poorly understood. Overexpression of each of the above EMT inducers up-regulates a subset of other EMT-inducing TFs, with Twist, Zeb1, Zeb2, TGF-beta1, and FOXC2 being commonly induced. Up-regulation of Slug and FOXC2 by either Snail or Twist does not depend on TGF-beta1 signaling. Gene expression signatures (GESs) derived by overexpressing EMT-inducing TFs reveal that the Twist GES and Snail GES are the most similar, although the Goosecoid GES is the least similar to the others. An EMT core signature was derived from the changes in gene expression shared by up-regulation of Gsc, Snail, Twist, and TGF-beta1 and by down-regulation of E-cadherin, loss of which can also trigger an EMT in certain cell types. The EMT core signature associates closely with the claudin-low and metaplastic breast cancer subtypes and correlates negatively with pathological complete response. Additionally, the expression level of FOXC1, another EMT inducer, correlates strongly with poor survival of breast cancer patients.

Fig. 1.

Expression of EMT marker genes in breast cancer cell lines and nontransformed EMT-induced human mammary epithelial cells. (A) Expression of EMT marker genes was measured by semiquantitative RT-PCR performed on RNA extracted from MCF-7, MDA-MB 231, MDA-MB 435, and SUM1315 cells. (B–D) HMLE cells were transduced with a retrovirus overexpressing the indicated gene and expression of the indicated gene was measured by semiquantitative RT-PCR (B) or quantitative PCR (C and D). GAPDH (C) or U6 snoRNA (D) was amplified for normalization. Error bars represent the SD from three independent experiments.

Fig. 2.

TGF-β1 is up-regulated by EMT inducers, but is not required for up-regulation of FOXC2 or Slug. (A) HMLE cells were transduced with a retrovirus overexpressing the indicated genes and expression of TGF-β1 mRNA was measured by quantitative RT-PCR. GAPDH was amplified for normalization. (B) HMLE cells transduced with a retrovirus overexpressing the indicated gene were treated with DMSO or SM16, and a TGF-β signaling inhibitor and gene expression was assayed by Western blot for the indicated proteins. Relative levels of pSmad2/3 were calculated by densitometry and listed beneath the bands. α-Actin was used as a loading control.

Fig. 3.

Clustering of the individual EMT-inducer gene expression profiles based on their similarity to each other and to a large cohort of breast cancer patient gene-expression samples. (A) Heatmap of gene expression profiles in each sample. Values represent the log2 ratio over control. The diagram above each heatmap shows the similarities of EMT-inducer profiles to each other. (B) Heat map of correlations of each EMT-inducer profile with the gene expression profiles of patient tumors in a large cohort of breast cancer patients (49). Pearson correlation coefficients were calculated for all of the EMT-inducer–patient pairs and plotted as a heat map, in which red indicates a highly significant positive correlation, green indicates a highly significant negative correlation and black indicates a weak or absent correlation.

Fig. 4.

The core EMT signature correlates with metaplastic and claudin-low breast cancers. (A and B) Gene-expression data were plotted as box plots for the mean expression of the EMT-up genes (A) and the EMT-down genes (B) by subtype using the dataset from Hennessey et al. (23) with the addition of 12 metaplastic tumors. Subtypes were called as in Herschkowitz et al. (22). The list was derived using Significance Analysis of Microarrays and cut off at the top ∼1,155 probes, 544 up and 611 down. Next, the genes were extracted in the dataset and averaged in each tumor (up and down separately). The one-way ANOVA significance for each plot was P < 0.0001.

Fig. 5.

EMT-inducing genes are up-regulated in metaplastic and claudin-low tumors and FOXC1 expression marks basal-like tumors and is a predictor of poor clinical outcome. (A) Data were extracted for EMT-related genes and samples were ordered by intrinsic subtype as in Herschkowitz et al. (22). The twelve metaplastic tumors from Hennessey et al. (23) were also included. (B) Patients from the NKI and UNC datasets were divided into high- and low-FOXC1 expressers and their survival was compared. The P value was generated using the χ² test of equality.