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Cyclins and cyclin-dependent kinases (CDKs) are hyperactivated in numerous human tumors. To identify means of interfering with cyclins/CDKs, we performed nine genome-wide screens for human microRNAs (miRNAs) directly regulating cell-cycle proteins. We uncovered a distinct class of miRNAs that target nearly all cyclins/CDKs, which are very effective in inhibiting cancer cell proliferation. By profiling the response of over 120 human cancer cell lines, we derived an expression-based algorithm that can predict the response of tumors to cell-cycle-targeting miRNAs. Using systemic administration of nanoparticle-formulated miRNAs, we inhibited tumor progression in seven mouse xenograft models, including three treatment-refractory patient-derived tumors, without affecting normal tissues. Our results highlight the utility of using cell-cycle-targeting miRNAs for treatment of refractory cancer types.
Pubmed ID: 28399412 RIS Download
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This monoclonal targets slightly modified β-cytoplasmic actin N-terminal peptide, Ac-Asp-Asp-Asp-Ile-Ala-Ala-Leu-Val-Ile-Asp-Asn-Gly-Ser-Gly-Lys, conjugated to KLH
View all literature mentionsThis polyclonal targets GAPDH
View all literature mentionsThis monoclonal targets Cdk6 antibody
View all literature mentionsThis monoclonal targets Cdk2 (D-12)
View all literature mentionsThis monoclonal targets CDK1 antibody [EPR165]
View all literature mentionsThis polyclonal targets Cyclin B1
View all literature mentionsThis monoclonal targets NEW Cyclin A2 clone 17B8.2
View all literature mentionsThis monoclonal targets Cyclin E2 clone EP454Y rabbit monoclonal
View all literature mentionsThis monoclonal targets cyclin E (HE12)
View all literature mentionsThis unknown targets Raised against peptide corresponding to an AA sequence at the C-terminus of human origin (Identical to corresponding sequence in mouse)
View all literature mentionsThis monoclonal targets Cyclin D1 clone EPR2241(IHC)-32 Rabbit
View all literature mentionsProject exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsA collaborative project between the Broad Institute and the Novartis Institutes for Biomedical Research and its Genomics Institute of the Novartis Research Foundation, with the goal of conducting a detailed genetic and pharmacologic characterization of a large panel of human cancer models. The CCLE also works to develop integrated computational analyses that link distinct pharmacologic vulnerabilities to genomic patterns and to translate cell line integrative genomics into cancer patient stratification. The CCLE provides public access to genomic data, analysis and visualization for about 1000 cell lines.
View all literature mentionsCollection of genome databases for vertebrates and other eukaryotic species with DNA and protein sequence search capabilities. Used to automatically annotate genome, integrate this annotation with other available biological data and make data publicly available via web. Ensembl tools include BLAST, BLAT, BioMart and the Variant Effect Predictor (VEP) for all supported species.
View all literature mentionsAn online database for miRNA target prediction and functional annotations.
View all literature mentionsData set of 2003 and 2005 miRNA-Target predictions for Drosophila miRNAs.
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Web tool to predict biological targets of miRNAs by searching for presence of conserved 8mer, 7mer and 6mer sites that match seed region of each miRNA. Nonconserved sites are also predicted and sites with mismatches in seed region that are compensated by conserved 3' pairing. Used to search for predicted microRNA targets in mammals.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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