Volume 5 Supplement 1

24th Congress of the International Association for Breast Cancer Research. Advances in human breast cancer research: preclinical models

Open Access

Genomic approaches to drug target discovery using mouse models

  • DW Morris1
Breast Cancer Research20035(Suppl 1):57

https://doi.org/10.1186/bcr716

Published: 1 October 2003

Cancer results from the accumulation of somatic mutations of proto-oncogenes and/or tumor suppressor genes during carcinogenesis. An important source of somatic mutations in some animal cancer models is provirus insertion mutation. Such mutations can arise from either retrovirus infection or retrotransposition of active proviral elements present in the germline, and result in the disruption and/or dysregulation of genes at or near sites of new provirus integration. This phenomenon serves as the basis for an experimental strategy for identifying cancer genes, called provirus tagging, in which proviruses act as both mutagens and tags for the subsequent identification of mutated genes that participate in carcinogenesis. In the mouse, two retroviruses cause cancer by this mechanism; mouse mammary tumor virus and murine leukemia virus. The development of PCR-based methods for efficiently recovering host/virus junction fragments from new proviral integrants, as well as the availability of the mouse genome sequence and new bioinformatics tools, has recently led to a dramatic advance in the power of this experimental strategy. As a consequence, large-scale provirus insertion mutation screens are now ongoing in at least three biotechnology companies and two public research centers. These screens demonstrate that mouse provirus insertion mutation models are able to efficiently identify genes involved in human cancer. Moreover, they suggest that an unexpectedly large fraction of the genes in the mammalian genome can contribute to the process of carcinogenesis. Because provirus tagging allows the precise molecular discrimination between cause and consequence, provirus insertion mutation models provide a useful new filter for cancer drug target selection.

Authors’ Affiliations

(1)
OncoNex Corporation, Davis

Copyright

© BioMed Central 2003

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