Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

Abstract

Background: One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively.

Methods and findings: Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER- patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome.

Conclusions: The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.

Figure 1. The 22 gene 3D signature predicts survival in the microarray datasets of Wang, et al., and Sorlie, et al.

The 22 gene signature and unsupervised hierarchical clustering grouped breast cancer patients to accurately reflect overall relapse or survival when analyzed by the method of Kaplan and Meier. A. Hierarchical cluster analysis of the dataset of Wang, et al. The pattern of expression of the 22 genes selected by the 3D assay are shown for the 286 breast cancer patients of Wang, et al. Genes and samples were organized by using hierarchical clustering. The two major clusters in the sample dimension (red cluster and yellow cluster), were found by using survival analysis to distinguish between good and poor prognosis patients (p<0.0001). B. Kaplan-Meier curves for the red and yellow clusters of the hierarchical diagram of panel A. The endpoint recorded for this dataset was relapse, measured in months. C. Hierarchical cluster analysis of Sorlie, et al. dataset. The pattern of expression of the 15 of 22 genes with probes on the Stanford microarrays and with data available for at least 40% of patients are shown for the 121 breast cancer patients reported by Sorlie, et al. Expression was organized by hierarchical clustering. The two major clusters in the sample dimension (red cluster and yellow cluster), were found by using survival analysis to distinguish between good and poor prognosis patients (p = 0.00447). D. Kaplan-Meier curves for the red and yellow clusters of the hierarchical diagram of panel C. The endpoint recorded for this dataset was death, measured in months.

Figure 2. Kaplan-Meier curves of the individual genes that accurately predicted patient prognosis (p<0.05).

A. Results for individual genes in the dataset of Wang, et al., using patient relapse as the endpoint. B. Results for individual genes in the dataset of Sorlie, et al., using patient survival as the endpoint.