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Detecting Cancer Outlier Genes with Potential Rearrangement Using Gene Expression Data and Biological Networks

DOI: 10.1155/2012/373506

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Abstract:

Gene alterations are a major component of the landscape of tumor genomes. To assess the significance of these alterations in the development of prostate cancer, it is necessary to identify these alterations and analyze them from systems biology perspective. Here, we present a new method (EigFusion) for predicting outlier genes with potential gene rearrangement. EigFusion demonstrated excellent performance in identifying outlier genes with potential rearrangement by testing it to synthetic and real data to evaluate performance. EigFusion was able to identify previously unrecognized genes such as FABP5 and KCNH8 and confirmed their association with primary and metastatic prostate samples while confirmed the metastatic specificity for other genes such as PAH, TOP2A, and SPINK1. We performed protein network based approaches to analyze the network context of potential rearranged genes. Functional gene rearrangement Modules are constructed by integrating functional protein networks. Rearranged genes showed to be highly connected to well-known altered genes in cancer such as AR, RB1, MYC, and BRCA1. Finally, using clinical outcome data of prostate cancer patients, potential rearranged genes demonstrated significant association with prostate cancer specific death. 1. Introduction Genetic alterations in cancer are the most challenging factors that might lead to aggressive behavior of cells. Among the most prevalent forms of genetic alterations observed in cancer cells are gene fusions, gene amplification, and gene deletions. Recurrent translocations generally fall into two categories: functional rearrangements that result in a change in gene's activity due either to a change in protein quality or quantity and the other category is silent translocations that have no effect on gene's activity. Functional translocations can be categorized into two subtypes; one that leads to fused transcripts resulting in new proteins with different activity like BCR-ABL in leukemia [1] and EML4-ALK in lung cancer [2]; on the other hand, it can lead to change in a transcript quantity by translocating a strong gene promoter to the intact coding region of an oncogene like TMPRSS2-ERG [3]. Another functional genomics rearrangement is genomic deletion which results in loss of DNA segment that might harbour functional genes. PTEN is a well-studied genomic deletion in prostate cancer that is anticipated to trigger a cascade of genomic rearrangements [4]. Figure 1 gives a schematic description of the four rearrangement types. Figure 1: Gene rearrangements, common gene rearrangements in

References

[1]  A. de Klein, A. G. van Kessel, G. Grosveld, et al., “A celllular oncogene is translocated to the Philadelphia chromosome in chronic myelocytic leukaemia,” Nature, vol. 300, no. 5894, pp. 765–767, 1982.
[2]  M. Soda, Y. L. Choi, M. Enomoto et al., “Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer,” Nature, vol. 448, no. 7153, pp. 561–566, 2007.
[3]  C. Kumar-Sinha, S. A. Tomlins, and A. M. Chinnaiyan, “Recurrent gene fusions in prostate cancer,” Nature Reviews Cancer, vol. 8, no. 7, pp. 497–511, 2008.
[4]  J. A. Squire, “TMPRSS2-ERG and PTEN loss in prostate cancer,” Nature Genetics, vol. 41, no. 5, pp. 509–510, 2009.
[5]  M. F. Berger, M. S. Lawrence, and F. Demichelis, “The genome complexity of primary human prostate cancer,” Nature, vol. 470, pp. 214–220, 2011.
[6]  R. I. Skotheim, G. O. S. Thomassen, M. Eken et al., “A universal assay for detection of oncogenic fusion transcripts by oligo microarray analysis,” Molecular Cancer, vol. 8, pp. 1–2, 2009.
[7]  H. Edgren, A. Murumagi, S. Kangaspeska et al., “Identification of fusion genes in breast cancer by paired-end RNA-sequencing,” Genome Biology, vol. 12, no. 1, article R6, 2011.
[8]  Y. Hu, K. Wang, X. He, D. Y. Chiang, J. F. Prins, and J. Liu, “A probabilistic framework for aligning paired-end RNA-seq data,” Bioinformatics, vol. 26, no. 16, pp. 1950–1957, 2010.
[9]  K. Wang, D. Singh, Z. Zeng et al., “MapSplice: accurate mapping of RNA-seq reads for splice junction discovery,” Nucleic Acids Research, vol. 38, no. 18, article e178, 2010.
[10]  A. McPherson, F. Hormozdiari, A. Zayed et al., “Defuse: an algorithm for gene fusion discovery in tumor rna-seq data,” PLoS Computational Biology, vol. 7, no. 5, Article ID e1001138, 2011.
[11]  M. A. Rubin and A. M. Chinnaiyan, “Bioinformatics approach leads to the discovery of the TMPRSS2:ETS gene fusion in prostate cancer,” Laboratory Investigation, vol. 86, no. 11, pp. 1099–1102, 2006.
[12]  W. U. Baolin, “Cancer outlier differential gene expression detection,” Biostatistics, vol. 8, no. 3, pp. 566–575, 2007.
[13]  L. Li, A. Chaudhuri, J. Chant, and Z. Tang, “PADGE: analysis of heterogeneous patterns of differential gene expression,” Physiological Genomics, vol. 32, no. 1, pp. 154–159, 2007.
[14]  S. A. Tomlins, D. R. Rhodes, S. Perner et al., “Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer,” Science, vol. 310, no. 5748, pp. 644–648, 2005.
[15]  R. Tibshirani and T. Hastie, “Outlier sums for differential gene expression analysis,” Biostatistics, vol. 8, no. 1, pp. 2–8, 2007.
[16]  J. P. Mpindi, H. Sara, S. Haapa-Paananen et al., “Gti: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets,” PLoS ONE, vol. 6, no. 2, Article ID e17259, 2011.
[17]  G. Wu, X. Feng, and L. Stein, “A human functional protein interaction network and its application to cancer data analysis,” Genome Biology, vol. 11, no. 5, article R53, 2010.
[18]  D. Singh, P. G. Febbo, K. Ross et al., “Gene expression correlates of clinical prostate cancer behavior,” Cancer Cell, vol. 1, no. 2, pp. 203–209, 2002.
[19]  B. Tylor, N. Schultz, H. Hieronyymus, and W. Gerald, “Integrative genomic profiling of human prostate cancer,” Cancer Cell, vol. 18, pp. 1–12, 2010.
[20]  A. D. Darnel, C. J. LaFargue, R. T. Vollmer, J. Corcos, and T. A. Bismar, “TMPRSS2-ERG fusion is frequently observed in gleason pattern 3 prostate cancer in a canadian cohort,” Cancer Biology and Therapy, vol. 8, no. 2, pp. 125–130, 2009.
[21]  S. A. Tomlins, D. R. Rhodes, J. Yu et al., “The role of SPINK1 in ETS rearrangement-negative prostate cancers,” Cancer Cell, vol. 13, no. 6, pp. 519–528, 2008.
[22]  S. R. Setlur, K. D. Mertz, Y. Hoshida et al., “Estrogen-dependent signaling in a molecularly distinct subclass of aggressive prostate cancer,” Journal of the National Cancer Institute, vol. 100, no. 11, pp. 815–825, 2008.
[23]  J. Momand, H. H. Wu, and G. Dasgupta, “MDM2—master regulator of the p53 tumor suppressor protein,” Gene, vol. 242, no. 1-2, pp. 15–29, 2000.
[24]  S. S. Myatt and E. W. F. Lam, “The emerging roles of forkhead box (Fox) proteins in cancer,” Nature Reviews Cancer, vol. 7, no. 11, pp. 847–859, 2007.
[25]  H. Maeda, S. Nagata, C. D. Wolfgang, G. L. Bratthauer, T. K. Bera, and I. Pastan, “The T cell receptor γ chain alternate reading frame protein (TARP), a prostate-specific protein localized in mitochondria,” Journal of Biological Chemistry, vol. 279, no. 23, pp. 24561–24568, 2004.
[26]  E. A. Morgan, S. S. Forootan, J. Adamson et al., “Expression of cutaneous fatty acid-binding protein (C-FABP) in prostate cancer: potential prognostic marker and target for tumourigenicity-suppression,” International Journal of Oncology, vol. 32, no. 4, pp. 767–775, 2008.

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