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Immunocompetent mouse allograft models for development of therapies to target breast cancer metastasis

  1. Author:
    Yang, Yuan
    Yang, Howard H
    Hu, Ying
    Watson, Peter H
    Liu, Huaitian
    Geiger, Thomas R
    Anver, Miriam R
    Haines, Diana
    Martin, Philip
    Green, Jeffrey E
    Lee, Maxwell P
    Hunter, Kent W
    Wakefield, Lalage M
  2. Author Address

    Lab of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA., High Dimension Data Analysis Group, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA., British Columbia Cancer Agency, Vancouver Island Center, Victoria, British Columbia, Canada., Pathology Histotechnology Lab, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick MD, USA.,
    1. Year: 2017
    2. Date: Feb 25
    3. Epub Date: 2017 Feb 25
  1. Journal: Oncotarget
  2. Type of Article: Article
  1. Abstract:

    Effective drug development to combat metastatic disease in breast cancer would be aided by the availability of well-characterized preclinical animal models that (a) metastasize with high efficiency, (b) metastasize in a reasonable time-frame, (c) have an intact immune system, and (d) capture some of the heterogeneity of the human disease. To address these issues, we have assembled a panel of twelve mouse mammary cancer cell lines that can metastasize efficiently on implantation into syngeneic immunocompetent hosts. Genomic characterization shows that more than half of the 30 most commonly mutated genes in human breast cancer are represented within the panel. Transcriptomically, most of the models fall into the luminal A or B intrinsic molecular subtypes, despite the predominance of an aggressive, poorly-differentiated or spindled histopathology in all models. Patterns of immune cell infiltration, proliferation rates, apoptosis and angiogenesis differed significantly among models. Inherent within-model variability of the metastatic phenotype mandates large cohort sizes for intervention studies but may also capture some relevant non-genetic sources of variability. The varied molecular and phenotypic characteristics of this expanded panel of models should aid in model selection for development of antimetastatic therapies in vivo, and serve as a useful platform for predictive biomarker identification.

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External Sources

  1. DOI: 10.18632/oncotarget.15695
  2. PMID: 28430642

Library Notes

  1. Fiscal Year: FY2016-2017
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