The molecular portraits of breast tumors are conserved across microarray platforms

Zhiyuan Hu, Cheng Fan, Daniel S Oh, J S Marron, Xiaping He, Bahjat F Qaqish, Chad Livasy, Lisa A Carey, Evangeline Reynolds, Lynn Dressler, Andrew Nobel, Joel Parker, Matthew G Ewend, Lynda R Sawyer, Junyuan Wu, Yudong Liu, Rita Nanda, Maria Tretiakova, Alejandra Ruiz Orrico, Donna Dreher, Juan P Palazzo, Laurent Perreard, Edward Nelson, Mary Mone, Heidi Hansen, Michael Mullins, John F Quackenbush, Matthew J Ellis, Olufunmilayo I Olopade, Philip S Bernard, Charles M Perou, Zhiyuan Hu, Cheng Fan, Daniel S Oh, J S Marron, Xiaping He, Bahjat F Qaqish, Chad Livasy, Lisa A Carey, Evangeline Reynolds, Lynn Dressler, Andrew Nobel, Joel Parker, Matthew G Ewend, Lynda R Sawyer, Junyuan Wu, Yudong Liu, Rita Nanda, Maria Tretiakova, Alejandra Ruiz Orrico, Donna Dreher, Juan P Palazzo, Laurent Perreard, Edward Nelson, Mary Mone, Heidi Hansen, Michael Mullins, John F Quackenbush, Matthew J Ellis, Olufunmilayo I Olopade, Philip S Bernard, Charles M Perou

Abstract

Background: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list.

Results: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups.

Conclusion: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.

Figures

Figure 1
Figure 1
Overview of the analysis methods and datasets used in this paper.
Figure 2
Figure 2
Hierarchical cluster analysis of the 315-sample combined test set using the Intrinsic/UNC gene set reduced to 306 genes. (A) Overview of complete cluster diagram. (B) Experimental sample-associated dendrogram. (C) Luminal/ER+ gene cluster with GATA3-regulated genes highlighted in pink. (D) HER2 and GRB7-containing expression cluster. (E) Interferon-regulated cluster containing STAT1. (F) Basal epithelial cluster. (G) Proliferation cluster.
Figure 3
Figure 3
Kaplan-Meier survival curves of breast tumors classified by intrinsic subtype. Survival curves are shown for (A) the 315-sample combined test set classified by hierarchical clustering using the Intrinsic/UNC gene set and (B) the 60-sample Ma et al., (C) 96-sample Chang et al., and (D) 105-sample (used to derive the Intrinsic/UNC gene set) datasets classified by the Nearest-Centroid predictor (Single Sample Predictor).
Figure 4
Figure 4
Kaplan-Meier survival curves using RFS as the endpoint, for the common clinical parameters present within the 315-sample combined test set. Survival curves are shown for (A) ER status, (B) node status, (C) histologic grade (1 = well-differentiated, 2 = intermediate, 3 = poor), and (D) tumor size (1 = diameter of 2 cm or less; 2 = diameter greater than 2 cm and less than or equal to 5 cm; 3 = diameter greater than 5 cm; 4 = any size with direct extension to chest wall or skin).

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