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Identification of Potential Driver Genes in Human Liver Carcinoma by Genomewide Screening

¹ Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland; ² Department of Systems Biology, Division of Cancer Medicine, University of Texas M. D. Anderson Cancer Center, Houston, Texas; and ³ Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea

Requests for reprints: Snorri S. Thorgeirsson, Center of Excellence in Integrative Cancer Biology and Genomics, Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, NIH, 37 Convent Drive MSC 4262, Building 37, Room 4146A, Bethesda, MD 20892-4262. Phone: 301-496-1935; Fax: 301-496-0734; E-mail: snorri_thorgeirsson{at}nih.gov.

Key Words: CGH • microarray • mTOR • AMPK • nicastrin

Genomic copy number aberrations and corresponding transcriptional deregulation in the cancer genome have been suggested to have regulatory roles in cancer development and progression. However, functional evaluation of individual genes from lengthy lists of candidate genes from genomic data sets presents a significant challenge. Here, we report effective gene selection strategies to identify potential driver genes based on systematic integration of genome scale data of DNA copy numbers and gene expression profiles. Using regional pattern recognition approaches, we discovered the most probable copy number–dependent regions and 50 potential driver genes. At each step of the gene selection process, the functional relevance of the selected genes was evaluated by estimating the prognostic significance of the selected genes. Further validation using small interference RNA–mediated knockdown experiments showed proof-of-principle evidence for the potential driver roles of the genes in hepatocellular carcinoma progression (i.e., NCSTN and SCRIB). In addition, systemic prediction of drug responses implicated the association of the 50 genes with specific signaling molecules (mTOR, AMPK, and EGFR). In conclusion, the application of an unbiased and integrative analysis of multidimensional genomic data sets can effectively screen for potential driver genes and provides novel mechanistic and clinical insights into the pathobiology of hepatocellular carcinoma. [Cancer Res 2009;69(9):4059–66]