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  • 2015-2019  (2)
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  • 1
    Keywords: Oncology ; Cancer Research ; Springer eBooks
    Description / Table of Contents: An Access Primer to Repositories of Cancer-related Genomic Big Data -- Building Portable and Reproducible Cancer Informatics Workflows: An RNA Sequencing Case Study -- Computational Analysis of Structural Variation in Cancer Genomes -- CORE: A Software Tool for Delineating Regions of Recurrent DNA Copy Number Alteration in Cancer -- Identification of Mutated Cancer Driver Genes on Unpaired RNA-Seq Samples -- A Computational Protocol for Detecting Somatic Mutations by Integrating DNA and RNA Sequencing -- Allele-specific Expression Analysis in Cancer Using Next Generation Sequencing Data -- Computational Analysis of lncRNA Function in Cancer -- Computational Methods for Identification of T Cell Neoepitopes in Tumors -- Computational and Statistical Analysis of Array-based DNA Methylation Data -- Computational Methods for Subtyping Of Tumors and their Applications for Deciphering Tumor Heterogeneity -- Statistically Supported Identification of Tumor Subtypes -- Computational Methods for Analysis of Tumor Clonality and Evolutionary History -- Predictive Modeling of Anti-cancer Drug Sensitivity from Genetic Characterizations -- In silico Oncology Drug Repositioning and Polypharmacology -- Modelling Growth of Tumours and their Spreading Behaviour using Mathematical Functions
    Abstract: This volume covers a wide variety of state of the art cancer-related methods and tools for data analysis and interpretation. Chapters were designed to attract a broad readership, ranging from active researchers in computational biology and bioinformatics developers, clinical oncologists, and anti-cancer drug developers wishing to rationalize their search for new compounds. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, installation instructions for computational tools discussed, explanations of the input and output formats, and illustrative examples of applications. Authoritative and cutting-edge, Cancer Bioinformatics: Methods and Protocols aims to support researchers performing computational analysis of cancer-related data
    Pages: X, 280 p. 89 illus., 64 illus. in color. : online resource.
    ISBN: 9781493988686
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  • 2
    Publication Date: 2018-01-17
    Description: A distinction between indolent and aggressive disease is a major challenge in diagnostics of prostate cancer. As genetic heterogeneity and complexity may influence clinical outcome, we have initiated studies on single tumor cell genomics. In this study, we demonstrate that sparse DNA sequencing of single-cell nuclei from prostate core biopsies is a rich source of quantitative parameters for evaluating neoplastic growth and aggressiveness. These include the presence of clonal populations, the phylogenetic structure of those populations, the degree of the complexity of copy-number changes in those populations, and measures of the proportion of cells with clonal copy-number signatures. The parameters all showed good correlation to the measure of prostatic malignancy, the Gleason score, derived from individual prostate biopsy tissue cores. Remarkably, a more accurate histopathologic measure of malignancy, the surgical Gleason score, agrees better with these genomic parameters of diagnostic biopsy than it does with the diagnostic Gleason score and related measures of diagnostic histopathology. This is highly relevant because primary treatment decisions are dependent upon the biopsy and not the surgical specimen. Thus, single-cell analysis has the potential to augment traditional core histopathology, improving both the objectivity and accuracy of risk assessment and inform treatment decisions.Significance: Genomic analysis of multiple individual cells harvested from prostate biopsies provides an indepth view of cell populations comprising a prostate neoplasm, yielding novel genomic measures with the potential to improve the accuracy of diagnosis and prognosis in prostate cancer. Cancer Res; 78(2); 348–58. ©2017 AACR.
    Print ISSN: 0008-5472
    Electronic ISSN: 1538-7445
    Topics: Medicine
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