Author of

• 11503

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  P2.03 - Poster Session 2 - Technology and Novel Development (ID 151)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Biology
  • Presentations: 1
  • Moderators:
  • Coordinates: 10/29/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 11509

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    P2.03-006 - Determining Read Origin of Next-Generation Sequencing Datasets from Lung Cancer Xenografts (ID 2647)

    09:30 - 09:30  |  Author(s): K. Britt
    • Abstract

    Background
    Next-generation sequencing (NGS) studies in cancer are often limited by the amount, quality and purity of tissue samples obtained from patients. In this situation, primary xenografts have proven useful in providing preclinical models. Although xenograft lines are maintained in immunodeficient mice, we and others have shown that they retain important characteristics that are irreversibly lost in cell culture. Since the stromal component of xenograft tumors is derived from the host, the presence of mouse DNA and RNA has the potential to limit the use of these models for next-generation sequencing (NGS) analysis.

    Methods
    We prospectively addressed this question in an established primary xenograft model of small cell lung cancer (SCLC), a malignancy that is almost always diagnosed using small biopsies or needle aspiration cytology. We first developed an in-silico strategy that separates human and mouse reads with at least 97% accuracy. We then compared NGS data from a series of primary xenograft models with clonally derived, stroma-free cell lines, and with published datasets derived from the same models.

    Results
    Starting with the NCI-H209 cell line as a reference sample, we show that low coverage whole genome analysis demonstrated remarkable concordance between published genome data and internal controls, despite the presence of mouse genomic DNA. NGS analysis of exon-capture DNA revealed that this enrichment procedure was highly species-specific, with less than 4% of reads aligning to the mouse genome. Human-specific expression profiling with RNA-Seq replicated array-based gene signatures, whereas mouse- transcript profiles correlated with published datasets from human cancer stroma.

    Conclusion
    Primary xenograft models may therefore be a useful NGS platform for cancers where tissue samples are limiting.