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      RANK- and c-Met-mediated signal network promotes prostate cancer metastatic colonization

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          Abstract

          Prostate cancer (PCa) metastasis to bone is lethal and there is no adequate animal model for studying the mechanisms underlying the metastatic process. Here, we report that receptor activator of NF-κB ligand (RANKL) expressed by PCa cells consistently induced colonization or metastasis to bone in animal models. RANK-mediated signaling established a premetastatic niche through a feed-forward loop, involving the induction of RANKL and c-Met, but repression of androgen receptor (AR) expression and AR signaling pathways. Site-directed mutagenesis and transcription factor (TF) deletion/interference assays identified common TF complexes, c-Myc/Max, and AP4 as critical regulatory nodes. RANKL–RANK signaling activated a number of master regulator TFs that control the epithelial-to-mesenchymal transition (Twist1, Slug, Zeb1, and Zeb2), stem cell properties (Sox2, Myc, Oct3/4, and Nanog), neuroendocrine differentiation (Sox9, HIF1α, and FoxA2), and osteomimicry (c-Myc/Max, Sox2, Sox9, HIF1α, and Runx2). Abrogating RANK or its downstream c-Myc/Max or c-Met signaling network minimized or abolished skeletal metastasis in mice. RANKL-expressing LNCaP cells recruited and induced neighboring non metastatic LNCaP cells to express RANKL, c-Met/activated c-Met, while downregulating AR expression. These initially non-metastatic cells, once retrieved from the tumors, acquired the potential to colonize and grow in bone. These findings identify a novel mechanism of tumor growth in bone that involves tumor cell reprogramming via RANK–RANKL signaling, as well as a form of signal amplification that mediates recruitment and stable transformation of non-metastatic bystander dormant cells.

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          Most cited references39

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          The transcriptional network for mesenchymal transformation of brain tumors

          Inference of transcriptional networks that regulate transitions into physiologic or pathologic cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumor aggressiveness in human malignant glioma but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here, we show that reverse-engineering and unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPβ and Stat3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPβ and Stat3 reprograms neural stem cells along the aberrant mesenchymal lineage whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumor aggressiveness. In human glioma, expression of C/EBPβ and Stat3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results reveal that activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.
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            VCAM-1 promotes osteolytic expansion of indolent bone micrometastasis of breast cancer by engaging α4β1-positive osteoclast progenitors.

            Breast cancer patients often develop locoregional or distant recurrence years after mastectomy. Understanding the mechanism of metastatic recurrence after dormancy is crucial for improving the cure rate for breast cancer. Here, we characterize a bone metastasis dormancy model to show that aberrant expression of vascular cell adhesion molecule 1 (VCAM-1), in part dependent on the activity of the NF-κB pathway, promotes the transition from indolent micrometastasis to overt metastasis. By interacting with the cognate receptor integrin α4β1, VCAM-1 recruits monocytic osteoclast progenitors and elevates local osteoclast activity. Antibodies against VCAM-1 and integrin α4 effectively inhibit bone metastasis progression and preserve bone structure. These findings establish VCAM-1 as a promising target for the prevention and inhibition of metastatic recurrence in bone. Copyright © 2011 Elsevier Inc. All rights reserved.
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              A data integration methodology for systems biology.

              Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.

                Author and article information

                Journal
                Endocr Relat Cancer
                Endocr. Relat. Cancer
                ERC
                Endocrine-Related Cancer
                Bioscientifica Ltd (Bristol )
                1351-0088
                1479-6821
                April 2014
                : 21
                : 2
                : 311-326
                Affiliations
                [1 ]Uro-Oncology Research, Department of Medicine Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center 8750 Beverly Blvd., Atrium 103, Los Angeles, California, 90048USA
                [2 ]Department of Surgery Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center Los Angeles, CaliforniaUSA
                [3 ]Department of Biomedical Sciences Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center Los Angeles, CaliforniaUSA
                [4 ]Biostatistics and Bioinformatics Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center Los Angeles, CaliforniaUSA
                [5 ]Department of Pathology School of Medicine, University of Alabama Birmingham, AlabamaUSA
                [6 ]Department of Pathology University of Pittsburgh Pittsburgh, PennsylvaniaUSA
                [7 ]Department of Biochemistry and Cell Biology Rice University Houston, TexasUSA
                Author notes
                Correspondence should be addressed to L W K Chung ( Leland.chung@ 123456cshs.org )
                Article
                ERC130548
                10.1530/ERC-13-0548
                3959765
                24478054
                9d713c9a-91f8-4b96-bc65-6cb2b0ee7abb
                © 2014 The authors

                This work is licensed under a Creative Commons Attribution 3.0 Unported License

                History
                : 23 December 2013
                : 6 January 2014
                Categories
                Research

                Oncology & Radiotherapy
                rankl,rank,c-met,prostate cancer,metastasis,cancer dormancy
                Oncology & Radiotherapy
                rankl, rank, c-met, prostate cancer, metastasis, cancer dormancy

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