Epigenetic Silencing Mediated through Activated PI3K/AKT Signaling in Breast Cancer

Human Cancer Genetics Program, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA.
Cancer Research (Impact Factor: 9.33). 03/2011; 71(5):1752-62. DOI: 10.1158/0008-5472.CAN-10-3573
Source: PubMed


Trimethylation of histone 3 lysine 27 (H3K27me3) is a critical epigenetic mark for the maintenance of gene silencing. Additional accumulation of DNA methylation in target loci is thought to cooperatively support this epigenetic silencing during tumorigenesis. However, molecular mechanisms underlying the complex interplay between the two marks remain to be explored. Here we show that activation of PI3K/AKT signaling can be a trigger of this epigenetic processing at many downstream target genes. We also find that DNA methylation can be acquired at the same loci in cancer cells, thereby reinforcing permanent repression in those losing the H3K27me3 mark. Because of a link between PI3K/AKT signaling and epigenetic alterations, we conducted epigenetic therapies in conjunction with the signaling-targeted treatment. These combined treatments synergistically relieve gene silencing and suppress cancer cell growth in vitro and in xenografts. The new finding has important implications for improving targeted cancer therapies in the future.

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Available from: Pei-Yin Hsu, May 02, 2014
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    • "DNA methylation is an important factor in heritable epigenetic regulation that has been shown to alter gene expression without changes in DNA sequence. DNA methylation has been associated with many important processes such as genomic imprinting and carcinogenesis [22, 23]. Likewise, differential analysis of gene expression has enabled researchers to find cancer-associated genes and other diseases [24, 25]. "
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    ABSTRACT: The inventions of microarray and next generation sequencing technologies have revolutionized research in genomics; platforms have led to massive amount of data in gene expression, methylation, and protein-DNA interactions. A common theme among a number of biological problems using high-throughput technologies is differential analysis. Despite the common theme, different data types have their own unique features, creating a "moving target" scenario. As such, methods specifically designed for one data type may not lead to satisfactory results when applied to another data type. To meet this challenge so that not only currently existing data types but also data from future problems, platforms, or experiments can be analyzed, we propose a mixture modeling framework that is flexible enough to automatically adapt to any moving target. More specifically, the approach considers several classes of mixture models and essentially provides a model-based procedure whose model is adaptive to the particular data being analyzed. We demonstrate the utility of the methodology by applying it to three types of real data: gene expression, methylation, and ChIP-seq. We also carried out simulations to gauge the performance and showed that the approach can be more efficient than any individual model without inflating type I error.
    Full-text · Article · Jun 2014 · Computational and Mathematical Methods in Medicine
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    • "The PI3K/Akt signaling pathway has frequently appeared in a variety of human cancers, such as non-small cell lung cancer (26), breast cancer (27), prostate cancer (9), ovarian cancer (28) and other physiological processes, such as epithelial-mesenchymal transition and hippocampal cell multiplication in tumor development and cancer (26,29). The PI3K/Akt signaling pathway regulates a variety of critical cellular functions, such as proliferation, growth, survival, apoptosis, tumor growth and angiogenesis (8,9). "
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    ABSTRACT: The aim of this study was to investigate whether the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway affects the implantation of mouse embryos by regulating the expression of RhoA. The expression of PI3K, Akt, phosphorylated (p-)Akt, phosphatase and tensin homolog (PTEN) and RhoA in the uterus of mice on day 5 of pregnancy (D5) and in pseudopregnant mice was examined by quantitative reverse transcription polymerase chain reaction (qRT-PCR), immunohistochemistry and western blot analysis. A functional analysis of these genes was also performed by the intrauterine injection with the PI3K inhibitor, LY294002, on day 2 of pregnancy (D2). The expression levels of PI3K, p-Akt, RhoA at the implantation site were higher than those at the inter-implantation site in the endometrium; however, opposite effects were observed for PTEN expression. The expression levels of the above genes in the pseudopregnant group and in the group injected with the PI3K/Akt inhibitor, LY294002, were markedly lower than those in the pregnant group. Functional experiments revealed that the number of implantation sites had been significantly decreased (P<0.05) following the intrauterine injection of the PI3K inhibitor, LY294002, on day 2 of gestation compared with the contralateral injection of phosphate-buffered saline (PBS). These results suggest that the PI3K/Akt signaling pathway affects embryo implantation by regulating the expression of RhoA.
    Full-text · Article · Mar 2014 · International Journal of Molecular Medicine
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    • "ChIP-seq of H3K27me3 and H3K9me2 in AKT1-tranfected MCF710A cells is obtained from our previous study [21]. The ChIP-seq of TCF7L2 in MCF7 and PANC1 cells were obtained from our previous study [32]. "
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    ABSTRACT: Many computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples. In this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells. Several advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at
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