An Integrated Bioinformatics Approach Identifies Elevated Cyclin E2 Expression and E2F Activity as Distinct Features of Tamoxifen Resistant Breast Tumors

Dai Y (2011) An Integrated Bioinformatics Approach Identifies Elevated Cyclin E2 Expression and E2F Activity as Distinct Features of Tamoxifen Resistant Breast Tumors. PLoS ONE 6(7): e22274. doi:10.1371/journal.pone.0022274 An Integrated Bioinformatics Approach Identifies Elevated Cyclin E2 Expression and E2F Activity as Distinct Features of Tamoxifen Resistant Breast Tumors Lei Huang 0 Shuangping Zhao 0 Jonna M. Frasor 0 Yang Dai 0 Christian Scho nbach, Kyushu Institute of Technology, Japan 0 1 Department of Bioengineering, University of Illinois at Chicago , Chicago , Illinois, United States of America, 2 Department of Physiology and Biophysics, University of Illinois at Chicago , Chicago, Illinois , United States of America Approximately half of estrogen receptor (ER) positive breast tumors will fail to respond to endocrine therapy. Here we used an integrative bioinformatics approach to analyze three gene expression profiling data sets from breast tumors in an attempt to uncover underlying mechanisms contributing to the development of resistance and potential therapeutic strategies to counteract these mechanisms. Genes that are differentially expressed in tamoxifen resistant vs. sensitive breast tumors were identified from three different publically available microarray datasets. These differentially expressed (DE) genes were analyzed using gene function and gene set enrichment and examined in intrinsic subtypes of breast tumors. The Connectivity Map analysis was utilized to link gene expression profiles of tamoxifen resistant tumors to small molecules and validation studies were carried out in a tamoxifen resistant cell line. Despite little overlap in genes that are differentially expressed in tamoxifen resistant vs. sensitive tumors, a high degree of functional similarity was observed among the three datasets. Tamoxifen resistant tumors displayed enriched expression of genes related to cell cycle and proliferation, as well as elevated activity of E2F transcription factors, and were highly correlated with a Luminal intrinsic subtype. A number of small molecules, including phenothiazines, were found that induced a gene signature in breast cancer cell lines opposite to that found in tamoxifen resistant vs. sensitive tumors and the ability of phenothiazines to down-regulate cyclin E2 and inhibit proliferation of tamoxifen resistant breast cancer cells was validated. Our findings demonstrate that an integrated bioinformatics approach to analyze gene expression profiles from multiple breast tumor datasets can identify important biological pathways and potentially novel therapeutic options for tamoxifen-resistant breast cancers. - Funding: This work was funded in part by the Chicago Biomedical Consortium(#C-019) with support from The Searle Funds at The Chicago Community Trust (http://www.chicagobiomedicalconsortium.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study. Competing Interests: The authors have declared that no competing interests exist. Estrogen action through estrogen receptor alpha (ER) is a critical regulator of breast cancer cell proliferation and survival. Tamoxifen is an ER antagonist that competitively inhibits the interaction of estrogen with ER and represses ER activity [1,2,3]. Tamoxifen has been the primary therapeutic choice in both early and advanced ER positive breast cancer patients since the 1970s. Unfortunately, up to 50% of patients with metastatic disease do not respond to first-line treatment with tamoxifen, and many who receive it as adjuvant therapy experience relapse despite an initial response. Understanding mechanisms by which resistance develops is an important task, which could lead to new therapeutic strategies to combat tumors resistant to endocrine therapy. Recently, microarray gene expression profiling of ER+ breast tumors has been used to identify gene signatures for prediction of clinical outcome of patients treated with tamoxifen [4,5,6,7,8]. For example, a 36-gene signature has been derived that can correctly classify up to 80% of patients into relapse or relapse-free groups [7]. Similarly, a 44-gene signature and a 181-gene signature of tamoxifen responsiveness have also been developed from profiling different tumor sets [5,8]. These gene expression studies were primarily focused on the identification of gene signatures associated with disease progression and clinical outcomes. Therefore, genes in the signatures are not necessarily directly involved in mediating sensitivity to tamoxifen or regulating tumor growth. Furthermore, the analyses of molecular functions of these signature genes have provided only limited insight into underlying mechanisms related to the treatment failure. For example, a preliminary functional analysis of the 36-gene signature in Chanrion et al. [7] indicates that there were 23 under-expressed and 13 were over-expressed genes in tumors from patients with relapse compared to tumors that were relapse free. The underexpressed genes were involved in cellular adhesion or invasion, immune responses, and ER negative regulation, whereas the overexpressed genes were involved in control of mitosis and cell cycle, DNA replication, DNA repair. The 44-gene signature was derived from a set of 81 DE genes that are involved in estrogen action, apoptosis, and extracellular matrix based on functional annotation [8]. On the other hand, the 181-genes in the signature developed by Loi et al. [5] was created from 13 biological clusters determined in the context of a curated list of published molecular interactions by Ingenuity Pathways Analysis (IPA). These clusters represent biological functions such as cell cycle, cell death, DNA repair and cancer inflammation among others. However, whether these functions were represented by the over- or under-expressed genes in the tamoxifen resistant tumors was not clear. Also of note, the three published gene signatures are comprised of distinctly different sets of genes with a small overlap, which presents challenges in deriving any potential mechanisms that may underlie the development of tamoxifen resistance. We therefore undertook a systematic analysis of three publically available microarray data sets to better understand the biological mechanisms that may contribute to a tamoxifen resistant phenotype [5,6,7]. Interestingly, there was little overlap between the three datasets in terms of individual genes that are differentially expressed in tamoxifen resistant vs. sensitive tumors. However, a variety of bioinformatics analyses revealed several functional commonalities in these gene sets, including enhanced cell cycle potential, elevated activity of the target genes of the E2F family of transcription factors, and a number of small molecules that can reverse expression of genes associated with tamoxifen resistance. Finally, we (...truncated)