Patient-derived xenograft models of ovarian/gynecologic tumors

L. Liang, I. Mercado-Uribe, N. Niu, Y. Jiang, W. Cheng, J. Zhang, G. B. Mills, C. Scott, A. K. Sood, J. Liu

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Patient-derived xenografts (PDXs) have become the most promising preclinical model for gynecologic malignances, especially ovarian cancer. To generate PDX models of ovarian cancer, fresh human tumors have been injected into different sites in mouse models, including the intraovarian bursa, intraperitoneal cavity, subcutaneous tissue, gonadal fat pad, mammary fat pad, and subrenal capsule. The engraftment of first-generation ovarian cancer PDXs usually takes 2-6. months. Success rates for first-generation ovarian cancer PDXs range from 25% to 80%, depending on many factors, including the type of immunocompromised mice, tumor histotypes, and implant sites. The ovarian cancer PDXs have been shown to retain the morphologic, immunophenotypic, and genomic characteristics of original human tumors. Moreover, the predictive value of ovarian cancer PDXs has been proved by recent studies that showed concordance between chemoresponse in tumor xenografts and patients.

    Original languageEnglish (US)
    Title of host publicationPatient Derived Tumor Xenograft Models
    Subtitle of host publicationPromise, Potential and Practice
    PublisherElsevier Inc.
    Pages257-271
    Number of pages15
    ISBN (Electronic)9780128040614
    ISBN (Print)9780128040102
    DOIs
    StatePublished - 2017

    Keywords

    • Mouse models
    • Ovarian cancer
    • Patient-derived xenograft
    • Personalized therapy
    • Preclinical models

    ASJC Scopus subject areas

    • Medicine(all)

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  • Cite this

    Liang, L., Mercado-Uribe, I., Niu, N., Jiang, Y., Cheng, W., Zhang, J., Mills, G. B., Scott, C., Sood, A. K., & Liu, J. (2017). Patient-derived xenograft models of ovarian/gynecologic tumors. In Patient Derived Tumor Xenograft Models: Promise, Potential and Practice (pp. 257-271). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-804010-2.00019-9