A transcriptional fingerprint of estrogen in human breast cancer predicts patient survival

Abstract

Estrogen signaling plays an essential role in breast cancer progression, and estrogen receptor (ER) status has long been a marker of hormone responsiveness. However, ER status alone has been an incomplete predictor of endocrine therapy, as some ER+ tumors, nevertheless, have poor prognosis. Here we sought to use expression profiling of ER+ breast cancer cells to screen for a robust estrogen-regulated gene signature that may serve as a better indicator of cancer outcome. We identified 532 estrogen-induced genes and further developed a 73-gene signature that best separated a training set of 286 primary breast carcinomas into prognostic subtypes by stepwise cross-validation. Notably, this signature predicts clinical outcome in over 10 patient cohorts as well as their respective ER+ subcohorts. Further, this signature separates patients who have received endocrine therapy into two prognostic subgroups, suggesting its specificity as a measure of estrogen signaling, and thus hormone sensitivity. The 73-gene signature also provides additional predictive value for patient survival, independent of other clinical parameters, and outperforms other previously reported molecular outcome signatures. Taken together, these data demonstrate the power of using cell culture systems to screen for robust gene signatures of clinical relevance.

Figure 1

(a) Heat map representation of 532 in vitro estrogen-regulated genes across three ER+, estrogen-sensitive breast cancer cell lines (MCF-7, T47-D, and BT-474) after 17β-estradiol treatment. Each row represents a gene, and each column represents a sample treated with estrogen for different time periods (0, 1, 2, 4, 8, 12, or 24 hours with replicates). Red indicates high expression; blue, low expression. (b–d) Molecular concept map (MCM) analysis of the 532 estrogen-regulated genes (yellow node with black frame) showing enrichment networks of (b) previously reported estrogen-regulated molecular concepts both in vitro and in vivo, (c) gene ontology concepts, and (d) breast cancer progression and prognosis concepts. Each node represents a molecular concept or a set of biologically related genes. The node size is proportional to the number of genes in the concept. Each edge represents a statistically significant enrichment (see Table W1 for P values). Concepts of upregulated genes by estrogen treatment are indicated by light green nodes. Blue, holly green, and purple nodes represent genes upregulated in ER+ breast cancer, high-grade breast cancer, and breast cancer patients with poor outcome, respectively. Enriched gene ontology terms are represented by orange nodes.

Figure 2

Estrogen-regulated genes stratified breast cancer samples into two groups with significantly different prognoses. (a) Representation of stepwise cross-validation on the training set of Wang et al. Left panel, the number of misclassified samples by cross-validation. Right panel, survival difference of the resulted two clusters when a particular set of genes were used. x axis, the number of top genes, ordered by their corresponding survival significance. Dashed line, threshold used to select the optimal gene signature. (b) K-mean clustering representation of the 73 estrogen-regulated genes in the training cohort (left) and its Kaplan-Meier survival plot (right). The 73 genes were selected based on minimal misclassification error by 10-fold cross validation in the space of the initial identified 532 genes.

Figure 3

The 73-gene outcome signature predicts clinical outcome of breast cancer. The 73-gene signature was applied to predict individual test samples as either “low-risk” or “high-risk” for the studies of (a) van 't Veer et al., (b) Pawitan et al., (c) van de Vijver et al., (d) Miller et al., (e) Sotirious et al., (f) Bild et al., (g) Oh et al., (h) Sorlie et al., (i) Takahashi et al., (j) Ma et al., (k) Minn et al. Kaplan-Meier analysis was used to evaluate the significance of outcome difference between the two groups. P values were calculated by the log-rank test.

Figure 4

The 73-gene outcome signature predicts clinical outcome of ER+ breast cancer. The ER+ breast cancer samples were respectively extracted from the studies of (a) Wang et al., (b) van 't Veer et al., (c) van de Vijver et al., (d) Miller et al., (e) Sotirious et al., (f) Bild et al., (g) Oh et al., (h) Sorlie et al., (i) Takahashi et al. The significance of outcome difference between the low-risk and high-risk groups were estimated by KM survival analysis. P values were calculated by the log-rank test. The data set of Ma et al. is not included in this analysis as nearly all of its samples are ER+ and thus have been presented in Figure 3j.

Figure 5

The 73-gene outcome signature predicts clinical outcome in tamoxifen-treated (a–c) breast cancer subcohorts, (d–f) gliomas, and (g) lung adenocarcinoma. The low-risk and high-risk groups were predicted by the 73-gene signature with nearest centroid classification. KM analysis was used to evaluate the significance of outcome difference between the two groups. P values were calculated by the log-rank test.