Cancer Institute A national cancer institute
designated cancer center

Dean W. Felsher

Publication Details

  • Comparative genomic hybridization on mouse cDNA microarrays and its application to a murine lymphoma model ONCOGENE Sander, S., Bullinger, L., Karlsson, A., Giuriato, S., Hernandez-Boussard, T., Felsher, D. W., Pollack, J. R. 2005; 24 (40): 6101-6107


    Microarray-based formats offer a high-resolution alternative to conventional, chromosome-based comparative genomic hybridization (CGH) methods for assessing DNA copy number alteration (CNA) genome-wide in human cancer. For murine tumors, array CGH should provide even greater advantage, since murine chromosomes are more difficult to individually discern. We report here the adaptation and evaluation of a cDNA microarray-based CGH method for the routine characterization of CNAs in murine tumors, using mouse cDNA microarrays representing approximately 14,000 different genes, thereby providing an average mapping resolution of 109 kb. As a first application, we have characterized CNAs in a set of 10 primary and recurrent lymphomas derived from a Myc-induced murine lymphoma model. In primary lymphomas and more commonly in Myc-independent relapses, we identified a recurrent genomic DNA loss at chromosome 3G3-3H4, and recurrent amplifications at chromosome 3F2.1-3G3 and chromosome 15E1/E2-15F3, the boundaries of which we defined with high resolution. Further, by profiling gene expression using the same microarray platform, we identified within CNAs the relevant subset of candidate cancer genes displaying comparably altered expression, including Mcl1 (myeloid cell leukemia sequence 1), a highly expressed antiapoptotic gene residing within the chr 3 amplicon peak. CGH on mouse cDNA microarrays therefore represents a reliable method for the high-resolution characterization of CNAs in murine tumors, and a powerful approach for elucidating the molecular events in tumor development and progression in murine models.

    View details for DOI 10.1038/sj.onc.1208751

    View details for Web of Science ID 000231718100004

    View details for PubMedID 16007205

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