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  • 1
    Publication Date: 2016-07-16
    Description: Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of 〉10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 2
    Publication Date: 2016-01-07
    Description: Accumulating evidence has demonstrated that rewiring of metabolism in cells is an important hallmark of cancer. The percentage of patients killed by metabolic disorder has been estimated to be 30% of the advanced-stage cancer patients. Thus, a systematic annotation of cancer cell metabolism genes is imperative. Here, we present ccmGDB (Cancer Cell Metabolism Gene DataBase), a comprehensive annotation database for cell metabolism genes in cancer, available at http://bioinfo.mc.vanderbilt.edu/ccmGDB . We assembled, curated, and integrated genetic, genomic, transcriptomic, proteomic, biological network and functional information for over 2000 cell metabolism genes in more than 30 cancer types. In total, we integrated over 260 000 somatic alterations including non-synonymous mutations, copy number variants and structural variants. We also integrated RNA-Seq data in various primary tumors, gene expression microarray data in over 1000 cancer cell lines and protein expression data. Furthermore, we constructed cancer or tissue type-specific, gene co-expression based protein interaction networks and drug-target interaction networks. Using these systematic annotations, the ccmGDB portal site provides 6 categories: gene summary, phenotypic information, somatic mutations, gene and protein expression, gene co-expression network and drug pharmacological information with a user-friendly interface for browsing and searching. ccmGDB is developed and maintained as a useful resource for the cancer research community.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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