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    Next-gen sequencing analysis and algorithms for PDX and CDX models.

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    Authors
    Khandelwal, Garima
    Girotti, Maria Romina
    Smowton, Christopher
    Taylor, Sam
    Wirth, Chris
    Dynowski, Marek
    Frese, Kristopher K
    Brady, Ged
    Dive, Caroline
    Marais, Richard
    Miller, Crispin J
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    Affiliation
    RNA Biology Group, Cancer Research UK Manchester Institute
    Issue Date
    2017-04-25
    
    Metadata
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    Abstract
    Patient-derived xenograft (PDX) and CTC-derived explant (CDX) models are powerful methods for the study of human disease. In cancer research, these methods have been applied to multiple questions including the study of metastatic progression, genetic evolution and therapeutic drug responses. Since PDX and CDX models can recapitulate the highly heterogeneous characteristics of a patient tumor, as well as their response to chemotherapy, there is considerable interest in combining them with next-generation sequencing (NGS) in order to monitor the genomic, transcriptional, and epigenetic changes that accompany oncogenesis. When used for this purpose, their reliability is highly dependent on being able to accurately distinguish between sequencing reads that originate from the host, and those that arise from the xenograft itself. Here we demonstrate that failure to correctly identify contaminating host reads, when analyzing DNA- and RNA-sequencing (DNA-Seq and RNA-Seq) data from PDX and CDX models is a major confounding factor that can lead to incorrect mutation calls and a failure to identify canonical mutation signatures associated with tumorigenicity. In addition, a highly sensitive algorithm and open source software tool for identifying and removing contaminating host sequences is described. Importantly, when applied to PDX and CDX models of melanoma, these data demonstrate its utility as a sensitive and selective tool for the correction of PDX- and CDX-derived whole exome and RNA-Seq data.
    Citation
    Next-gen sequencing analysis and algorithms for PDX and CDX models. 2017 Mol. Cancer Res.
    Journal
    Molecular cancer research : MCR
    URI
    http://hdl.handle.net/10541/620344
    DOI
    10.1158/1541-7786.MCR-16-0431
    PubMed ID
    28442585
    Type
    Article
    Language
    en
    ISSN
    1557-3125
    ae974a485f413a2113503eed53cd6c53
    10.1158/1541-7786.MCR-16-0431
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    All Paterson Institute for Cancer Research

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