NEW*TCGA Network Identifies More Than 6,000 Targets for Sequencing
The Cancer Genome Atlas (TCGA) network has selected more than 6,000 gene and miRNA targets for sequencing that represent both protein-coding genes and microRNAs (miRNAs). While not exhaustive, this list represents genes and sequences with a potential for being associated with human cancers based on published and unpublished research.
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Molecular Characterization Data for Ovarian Cancer are Now Available
Molecular characterization data sets for ovarian cancer are now available via the TCGA Data Portal. The Data Portal also includes molecular characterization, high-throughput sequencing, and clinical data for glioblastoma multiforme (GBM). New data are derived and deposited in the TCGA Data Portal on an ongoing basis for download and analysis.
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The Cancer Genome Atlas Reports First Results of Comprehensive Study of Brain Tumors: Large-Scale Effort Identifies New Genetic Mutations, Core Pathways
The Cancer Genome Atlas Research Network reported the first results of its large-scale, comprehensive study of the most common form of brain cancer, glioblastoma (GBM) in the Sept. 4, 2008 advance online edition of the journal Nature. Among the TCGA findings are the identification of many gene mutations involved in GBM, including three previously unrecognized mutations that occur with significant frequency; and the delineation of core pathways disrupted in this type of brain cancer. One of the most exciting results is an unexpected observation that points to a potential mechanism of resistance to a common chemotherapy drug used for brain cancer.
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TCGA Moving Molecular Oncology Forward
The Cancer Genome Atlas (TCGA) pilot project, a collaborative effort between the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), has made valuable progress as new data is continually generated and shared internationally among researchers. With new technologies and tools being developed, TCGA is enabling researchers to delve further into the molecular machinery of cancer with greater precision and efficiency.
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