Changes in breast cancer transcriptional profiles after treatment with the aromatase inhibitor, letrozole

Breast Research Group, University of Edinburgh, Edinburgh, UK.
Pharmacogenetics and Genomics (Impact Factor: 3.48). 10/2007; 17(10):813-26. DOI: 10.1097/FPC.0b013e32820b853a
Source: PubMed


The aim of the study was to identify changes in tumour expression profiling associated with short-term therapy of breast cancer patients with letrozole.
Microarray analysis was performed on RNA extracted from paired tumour core biopsies taken before and after 14 days of treatment with letrozole (2.5 mg/daily) in 58 patients. Changes in expression profile were identified by three different approaches on the basis of frequency of changes, magnitude of changes and significance analysis of microarray.
No single gene was consistently changed by therapy in all cases. Fifty-two genes, however, were downregulated and 36 upregulated in at least 45 of the 58 cases. In terms of quantitative change, 46 genes showed at least a median 1.5-fold change in expression. Significance analysis of microarray identified 62 genes that were significantly changed by therapy (P<0.0001, 56 downregulated and six upregulated). All three approaches showed that greater numbers of genes were downregulated rather than upregulated. Merging data produced a total of 143 genes, which were subject to gene ontology and cluster analysis. The ontology of the 91 downregulated genes showed that they were functionally associated with cell cycle progression, particularly mitosis. In contrast, upregulated genes were associated with organ development, connective tissue extracellular matrix regulation and inflammatory response. Cluster analysis segregated the patients into four groups differing in patterns of gene expression.
Genes have been identified which either change markedly or consistently in breast cancer after 14 days treatment with letrozole. These are new important data in understanding letrozole's molecular mechanism of action in breast cancers.

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Available from: Alexey A Larionov
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    • "The initial analysis was performed with transcriptomic data generated from core biopsies of ER+ breast tumors at diagnosis (n = 58) and again following a 90-day course of neoadjuvant treatment with the drug letrozole (n = 60) [18,19]. Inclusion criteria required the samples to contain at least 20% malignant tissue. "
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    ABSTRACT: Background Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. Methods We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER+ breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). Results We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. Conclusions To derive the greatest benefit from molecularly targeted drugs it is critical to design combination treatment strategies rationally. Incorporating knowledge of the tumor adaptation process into the design provides an opportunity to match targeted drugs to the evolving tumor phenotype and surmount resistance.
    Full-text · Article · Apr 2014 · Genome Medicine
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    • "Transcriptional profiles were generated from core biopsies extracted from ER + breast tumors during the course of neoadjuvant treatment with the aromatase inhibitor letrozole [6,7]. Tumors were sampled before treatment (n = 58), following 14 days (n = 58), and following 90 days (n = 60) on drug. "
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    ABSTRACT: The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. We use this approach to prioritize genes as drug target candidates in a set of ER+ breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER+ breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use.
    Full-text · Article · Feb 2014 · BMC Systems Biology
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    • "Next, with the aim of comparing the observed gene expression changes following estrogen deprivation in breast cancer cells to patients who received aromatase inhibitor (AI) treatment, we analysed a publicly available array data set consisting of 58 postmenopausal breast cancer patients with array profiles assessed before and after neoadjuvant treatment with letrozole (Gene expression omnibus number: GSE5462) [26]. "
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    ABSTRACT: Long-term estrogen deprivation models are widely employed in an in-vitro setting to recapitulate the hormonal milieu of breast cancer patients treated with endocrine therapy. Despite the wealth information we have garnered from these models thus far, a comprehensive time-course analysis of the estrogen (ER), progesterone (PR), and human epidermal growth factor 2 (HER-2/neu) receptors on the gene and protein level, coupled with expression array data is currently lacking. We aimed to address this knowledge gap in order to enhance our understanding of endocrine therapy resistance in breast cancer patients. ER positive MCF7 and BT474 breast cancer cells were grown in estrogen depleted medium for 10 months with the ER negative MDA-MB-231 cell line employed as control. ER, PR and HER-2/neu expression were analysed at defined short and long-term time points by immunocytochemistry (ICC), and quantitative real-time RT-PCR (qRT-PCR). Microarray analysis was performed on representative samples. MCF7 cells cultured in estrogen depleted medium displayed decreasing expression of ER up to 8 weeks, which was then re-expressed at 10 months. PR was also down-regulated at early time points and remained so for the duration of the study. BT474 cells generally displayed no changes in ER during the first 8 weeks of deprivation, however its expression was significantly decreased at 10 months. PR expression was also down-regulated early in BT474 samples and was absent at later time points. Finally, microarray data revealed that genes and cell processes down-regulated in both cell lines at 6 weeks overlapped with those down-regulated in aromatase inhibitor treated breast cancer patients. Our data demonstrate that expression of ER, PR, and cell metabolic/proliferative processes are unstable in response to long-term estrogen deprivation in breast cancer cell lines. These results mirror recent clinical findings and again emphasize the utility of LTED models in translational research.
    Full-text · Article · Oct 2013 · BMC Cancer
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