Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies
Brian D. Lehmann, … , Yu Shyr, Jennifer A. Pietenpol
Brian D. Lehmann, … , Yu Shyr, Jennifer A. Pietenpol
Published June 1, 2011
Citation Information: J Clin Invest. 2011;121(7):2750-2767. https://doi.org/10.1172/JCI45014.
View: Text | PDF
Research Article

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Abstract

Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem–like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted “driver” signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

Authors

Brian D. Lehmann, Joshua A. Bauer, Xi Chen, Melinda E. Sanders, A. Bapsi Chakravarthy, Yu Shyr, Jennifer A. Pietenpol

×

Figure 2

Identification of TNBC subtypes.

Options: View larger image (or click on image) Download as PowerPoint
Identification of TNBC subtypes. (A) Silhouette plot showing the composi...
(A) Silhouette plot showing the composition (n = number of tumors) and stability (AVG width) of k-means clustering on the TNBC training set. Clusters with s(I) > 0 were considered stable. (B) Consensus clustering displaying the robustness of sample classification using multiple iterations (×1000) of k-means clustering. (C) The CDF depicting the cumulative distribution from consensus matrices at a given cluster number (k). (D) The optimal cluster number is 7 at the point in which the relative change in area (Δ) under the CDF plot does not change with increasing k. (E) Principal component analysis graphically depicting fundamental differences in GE between the TNBC clusters. The cluster (subtypes) are named as shown.