Insertional Mutagenesis for Generating Mouse Models of Cancer

  • David A. Largaespada


Methods for modeling cancer in mice are extremely varied. Exposure to chemicals, radiation, or viruses, and the use of transgenic technologies have been employed, sometimes in combinations, to generate models of nearly every type of cancer that afflicts people. In some cases, scientists have sought information about the cancer causing capabilities of specific agents, including specific types and doses of radiation, chemicals or specific genes or mutant genes. In other studies, specific genes that can cause cancer were sought. Some approaches start with specific candidate genes. We can consider these approaches “reverse cancer genetics,” that is, studies that start with one or more specific genes whose biological activities will be studied in a mouse. In many cases, the genes studied came from those suspected to contribute to human cancer when mutated or misexpressed in some way. A completely different type of approach for modeling cancer asks “what genes, when mutated, can cause cancer?” These “forward cancer genetics” approaches frequently use random, somatic insertional mutagenesis to induce or accelerate cancer in mice. Thus, cancer genes can be discovered by looking for somatically acquired, tumor-specific insertion mutations near or within genes in tumor genomic DNA. By studying a whole panel of tumors induced this way, one can find genes that are altered by insertion mutation in multiple, independent tumors and thus discover excellent cancer gene candidates and cancer pathways. This chapter reviews available approaches for modeling cancer by insertional mutagenesis in mice. Details about setting up such a screen, identifying insertion sites, interpreting the results and leveraging the information gained to better understand tumor progression and human cancer are described.


Acute Myeloid Leukemia Long Terminal Repeat Insertion Mutation Insertional Mutagenesis Sleep Beauty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



I apologize to colleagues whose work I could not discuss here due to space limitations. I thank the members of the Largaespada laboratory and the Center for Genome Engineering for constant support and helpful discussions. Research in the Largaespada laboratory is supported by the National Institutes of Health (R01 CA113636-01A1 and UO1 CA84221), the American Cancer Society (RPG LIB-106632), and the Leukemia and Lymphoma Society of America (LLS 7019).


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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.The Department of Genetics, Cell Biology and Development, The Center for Genome Engineering, Masonic Cancer CenterThe University of MinnesotaMinneapolisUSA

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