Human Cancer Biology Identification of Novel Kinase Targets for the Treatment of Estrogen Receptor – Negative Breast Cancer

Purpose: Previous gene expression profiling studies of breast cancer have focused on the entire genome to identify genes differentially expressed between estrogen receptor (ER) α–positive and ER-α–negative cancers. Experimental Design: Here, we used gene expression microarray profiling to identify a distinct kinase gene expression profile that identifies ER-negative breast tumors and subsets ER-negative breast tumors into four distinct subtypes. Results: Based on the types of kinases expressed in these clusters, we identify a cell cycle regulatory subset, a S6 kinase pathway cluster, an immunomodulatory kinase–expressing cluster, and a mitogen-activated protein kinase pathway cluster. Furthermore, we show that this specific kinase profile is validated using independent sets of human tumors and is also seen in a panel of breast cancer cell lines. Kinase expression knockdown studies show that many of these kinases are essential for the growth of ER-negative, but not ERpositive, breast cancer cell lines. Finally, survival analysis of patients with breast cancer shows that the S6 kinase pathway signature subtype of ER-negative cancers confers an extremely poor prognosis, whereas patients whose tumors express high levels of immunomodulatory kinases have a significantly better prognosis. Conclusions: This study identifies a list of kinases that are prognostic and may serve as druggable targets for the treatment of ER-negative breast cancer. (Clin Cancer Res 2009;15(20):6327–40) The genomic era has produced an exponential increase in our understanding of cancer biology and has greatly accelerated cancer drug development. With the advent and implementation of microarray expression profiling, it is now possible to evaluate gene expression in tumors on a genome-wide basis. Gene expression analysis is now extensively used to subtype cancers, predict prognosis and disease-free survival, and determine optimal treatment (1–7). Estrogen receptor (ER) α–positive breast cancers account for 60% to 70% of breast cancers, but the remaining 30% to 40% of breast cancers are ER negative and are poorly responsive to traditional therapies (8). Selective ER modulators, such as tamoxifen and raloxifene, and aromatase inhibitors are currently used to treat ER-positive breast cancer and have been shown to reduce ER-positive breast cancer recurrence by ∼50% (9). These agents, however, are not effective in treating ER-negative breast cancer. Currently, chemotherapy is used to treat ER-negative tumors (10). Such therapy is generally toxic and is not specifically targeted to ER-negative breast cancer. Amajor goal of current breast cancer research has been to identify targets that are unique to cancer cells and to identify drugs that kill only cancerous cells without affecting normal tissue. Although achieving this goal has been difficult, there are several examples of effective targeted therapies, including development of the monoclonal antibodies trastuzumab (targeting the HER2/neu receptor) and bevacizumab (targeting vascular epithelial growth factor), which have been shown to be effective in treating breast cancer (11, 12). Other successes include the development of small-molecule tyrosine kinase inhibitors, including gefitinib and erlotinib (both of which target the epidermal growth factor receptor) and lapatinib (a dual-kinase inhibitor targeting both the epidermal growth factor receptor and the HER2/neu receptor; refs. 13–16). Despite these advances, such therapies are effective only in the 10% to 15% of patients whose tumors overexpress HER2. To develop targeted therapies for the remaining ER-negative breast cancers, including the aggressive ER-negative, progesterone receptor (PR)–negative, and HER2-negative (“triple-negative”) breast cancers, we used Authors' Affiliations: Department ofMolecular and Cellular Biology, Lester and Sue Smith Breast Center, and Department of Medicine, Baylor College of Medicine, Houston, Texas; and Department of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts Received 5/1/09; revised 7/11/09; accepted 7/13/09; publishedOnlineFirst 10/6/09. Grant support: NIH/National Cancer Institute Breast Cancer Special Project of Research Excellence P50CA58183, Department of Defense predoctoral fellowship W81XWH-06-1-0715, Susan G. Komen Breast Cancer Foundation Promise Grant, and the Breast Cancer Research Foundation. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). J. Chang and P. Brown contributed equally to this work. Requests for reprints: Powel H. Brown, Breast Center, Baylor College of Medicine, One Baylor Plaza, BCM 600, Houston, TX 77030. Phone: 713798-1609; Fax: 713-798-1657; E-mail: pbrown@breastcenter.tmc. edu. F 2009 American Association for Cancer Research. doi:10.1158/1078-0432.CCR-09-1107 6327 Clin Cancer Res 2009;15(20) October 15, 2009 www.aacrjournals. org Research. on May 29, 2017. © 2009 American Association for Cancer clincancerres.aacrjournals. org Downloaded from Published OnlineFirst October 6, 2009; DOI: 10.1158/1078-0432.CCR-09-1107