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Related Concept Videos

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors
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Predicting drug responsiveness in human cancers using genetically engineered mice.

Jerry Usary1, Wei Zhao, David Darr

  • 1Lineberger Comprehensive Cancer Center, Department of Genetics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
|June 20, 2013
PubMed
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Gene expression signatures from mouse models predict anticancer drug response in human breast cancer patients, even outperforming existing biomarkers. This research highlights mice as valuable tools for discovering new cancer biomarkers.

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Area of Science:

  • Oncology
  • Genomics
  • Translational Research

Background:

  • Developing accurate predictors of anticancer drug response is crucial for personalized cancer therapy.
  • Mouse models, including genetically engineered mouse models (GEMM) and orthotopic syngeneic murine transplants (OST), offer valuable platforms for preclinical research due to their precise genetics and intact immune systems.

Purpose of the Study:

  • To develop gene expression-based predictors of anticancer drug response in human tumors using GEMM and OST.
  • To identify novel biomarkers for predicting treatment efficacy in breast cancer.

Main Methods:

  • Examined the efficacy of four chemotherapeutic or targeted anticancer drugs in mouse models of three breast cancer subtypes (Basal-like, Luminal B, Claudin-low).
  • Performed RNA expression profiling of murine tumors to develop treatment and response signatures.
  • Validated the predictive potential of identified signatures using human patient data.

Main Results:

  • Combination therapies demonstrated greater efficacy and life prolongation compared to single agents in most mouse models.
  • Identified two gene expression signatures from chemotherapy-treated murine tumors that predicted pathologic complete response to neoadjuvant anthracycline/taxane therapy in human breast cancer patients.
  • The murine-derived gene signatures showed predictive value independent of common clinical variables and other genomic signatures.

Conclusions:

  • Murine-derived gene signatures can serve as reliable predictors of anticancer drug response in human patients.
  • These findings underscore the utility of mouse models in discovering novel biomarkers for human cancer treatment.
  • The study supports the translation of findings from mouse models to clinical applications in oncology.