Abstract
The systematic integration of expression profiles and other types of gene information, such as copy number, chromosomal localization, and sequence characteristics, still represents a challenge in the genomic arena. In particular, the integrative analysis of genomic and transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with structural and transcriptional imbalances often characterizing cancer.
A computational framework based on locally adaptive statistical procedures (Global Smoothing Copy Number, GLSCN, and Locally Adaptive Statistical Procedure, LAP), which incorporate genomic and transcriptional data with structural information for the identification of imbalanced chromosomal regions, is described. Both GLSCN and LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of copy number and gene expression signals. The application of GLSCN and LAP to the integrative analysis of a human metastatic clear cell renal carcinoma cell line (Caki-1) allowed identifying chromosomal regions that are directly involved in known chromosomal aberrations characteristic of tumors.
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References
Beer, M.A., Tavazoie, S.: Predicting gene expression from sequence. Cell. 117, 185–198 (2004)
Caron, H., van Schaik, B., van der Mee, M., Baas, F., Riggins, G., van Sluis, P., Hermus, M.C., van Asperen, R., Boon, K., Voute, P.A., Heisterkamp, S., van Kampen, A., Versteeg, R.: The human transcriptome map: clustering of highly expressed genes in chromosomal domains. Science 291, 1289–1292 (2001)
Versteeg, R., van Schaik, B.D., van Batenburg, M.F., Roos, M., Monajemi, R., Caron, H., Bussemaker, H.J., van Kampen, A.H.: The human transcriptome map reveals extremes in gene density, intron length, GC content, and repeat pattern for domains of highly and weakly expressed genes. Genome Res. 13, 1998–2004 (2003)
Garraway, L.A., Widlund, H.R., Rubin, M.A., Getz, G., Berger, A.J., Ramaswamy, S., Beroukhim, R., Milner, D.A., Granter, S.R., Du, J., et al.: Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436(7047), 117–122 (2005)
Tsafrir, D., Bacolod, M., Selvanayagam, Z., Tsafrir, I., Shia, J., Zeng, Z., Liu, H., Krier, C., Stengel, R.F., Barany, F., et al.: Relationship of gene expression and chromosomal abnormalities in colorectal cancer. Cancer Res. 66(4), 2129–2137 (2006)
Kotliarov, Y., Steed, M.E., Christopher, N., Walling, J., Su, Q., Center, A., Heiss, J., Rosenblum, M., Mikkelsen, T., Zenklusen, J.C., et al.: High-resolution Global Genomic Survey of 178 Gliomas Reveals Novel Regions of Copy Number Alteration and Allelic Imbalances. Cancer Res. 66(19), 9428–9436 (2006)
Bignell, G.R., Huang, J., Greshock, J., Watt, S., Butler, A., West, S., Grigorova, M., Jones, K.W., Wei, W., Stratton, M.R., et al.: High-resolution analysis of DNA copy number using oligonucleotide microarrays. Genome Res. 14(2), 287–295 (2004)
Crawley, J.J., Furge K.A.: Identification of frequent cytogenetic aberrations in hepatocellular carcinoma using gene-expression microarray data. Genome Biology 3, RESEARCH0075 (2002)
Toedling, J., Schmeier, S., Heinigm, M., Georgi, B., Roepcke, S.: MACAT microarray chromosome analysis tool. Bioinformatics 21(9), 2112–2113 (2005)
Levin, A.M., Ghosh, D., Cho, K.R., Kardia, S.L.: A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors. Bioinformatics 21(12), 2867–2874 (2005)
Callegaro, A., Basso, D., Bicciato, S.A.: locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions. Bioinformatics 22(21), 2658–2666 (2006)
Herrmann, E.: Local bandwidth choice in kernel regression estimation. Journal of Graphical and Computational Statistics 6, 35–54 (1997)
Gasser, T., Mller, H.G.: Kernel estimation of regression functions. Smoothing Techniques for Curve Estimation. In: Lecture Notes in Math., vol. 757, pp. 23–68. springer, Heidelberg
Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98(9), 5116–5121 (2001)
Storey, J.D., Tibshirani, R.: Statistical significance for genome-wide experiments. Proc. Natl. Acad. Sci. USA 100(16), 9440–9445 (2003)
La Rosa, P., Viara, E., Hupe, P., Pierron, G., Liva, S., Neuvial, P., Brito, I., Lair, S., Servant, N., Robine, N., et al.: VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles. Bioinformatics 22(17), 2066–2073 (2006)
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Zampieri, M. et al. (2007). Locally Adaptive Statistical Procedures for the Integrative Analysis on Genomic and Transcriptional Data. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_87
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DOI: https://doi.org/10.1007/978-3-540-73400-0_87
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