Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-27T13:09:48.410Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis of the Growth Control Network Specific for Human LungAdenocarcinoma Cells

Published online by Cambridge University Press:  25 January 2012

G. Pinna ,
A. Zinovyev ,
N. Araujo ,
N. Morozova  and
A. Harel-Bellan
G. Pinna
Affiliation:
CNRS FRE 3377, CEA Saclay, Gif-sur-Yvette, F-91191 and Universite Paris-Sud, Gif-sur-Yvette, F-91191, France
A. Zinovyev
Affiliation:
Institut Curie, 26 rue d’Ulm, Paris, France INSERM, U900, F-75248 Paris, France Mines ParisTech, Centre for Computational Biology, F-77300 Fontainebleau, France
N. Araujo
Affiliation:
CNRS FRE 3377, CEA Saclay, Gif-sur-Yvette, F-91191 and Universite Paris-Sud, Gif-sur-Yvette, F-91191, France
N. Morozova*
Affiliation:
CNRS FRE 3377, CEA Saclay, Gif-sur-Yvette, F-91191 and Universite Paris-Sud, Gif-sur-Yvette, F-91191, France
A. Harel-Bellan
Affiliation:
CNRS FRE 3377, CEA Saclay, Gif-sur-Yvette, F-91191 and Universite Paris-Sud, Gif-sur-Yvette, F-91191, France
*
⋆Corresponding author. E-mail: morozova@vjf.cnrs.fr
Get access

Abstract

Many cancer-associated genes and pathways remain to be identified in order to clarify themolecular mechanisms underlying cancer progression. In this area, genome-wideloss-of-function screens appear to be powerful biological tools, allowing the accumulationof large amounts of data. However, this approach currently lacks analytical tools toexploit the data with maximum efficiency, for which systems biology methods analyzingcomplex cellular networks may be extremely helpful. In this article we report such asystems biology strategy based on the construction of a Network for a biological processand specific for a given cell system (cell type). The networks are created fromgenome-wide loss-of-function screen datasets. We also propose tools to analyze networkproperties. As one of the tools, we suggest a mathematical model for discriminationbetween two distinct cell processes that may be affected by knocking down the activity ofa gene, i. e., a decreased cell number may be caused by arrested cell proliferation orenhanced cell death. Next we show how this discrimination between the two cell processeshelps to construct two corresponding subnetworks. Finally, we demonstrate an applicationof the proposed strategy to the identification and characterization of putative novelgenes and pathways significant for the control of lung cancer cell growth, based on theresults of a genome-wide proliferation/viability loss-of-function screen of human lungadenocarcinoma cells.

Keywords

systems biologynetwork analysislung adenocarcinomagenome-wide screen
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 7 , Issue 1: Cancer modeling , 2012 , pp. 337 - 368
Copyright
© EDP Sciences, 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Creighton, C.J., Bromberg-White, J.L., Misek, D.E., Monsma, D.J., Brichory, F., Kuick, R., Giordano, T.J., Gao, W., Omenn, G.S., Webb, C.P., Hanash, S.M.. Analysis of tumor-host interactions by gene expression profiling of lung adenocarcinoma xenografts identifies genes involved in tumor formation. Mol Cancer Res., 3 (2005), No. 3, 119–29. CrossRefGoogle ScholarPubMed
Murakami, Y.. Functional cloning of a tumor suppressor gene, TSLC1, in human non-small cell lung cancer. Oncogene, 7 (2002), No. 21(45), 6936–48. CrossRefGoogle Scholar
Jiang, Y., Cui, L., Yie, T.A., Rom, W.N., Cheng, H., Tchou-Wong, K.M.. Inhibition of anchorage-independent growth and lung metastasis of A549 lung carcinoma cells by IkappaBbeta. Oncogene, 26 (2001), No. 20(18), 2254–63. CrossRefGoogle Scholar
Soda, M., et al. Identification of the transforming EML4-ALK fusion gene in non-small cell lung cancer. Nature, 448 (2007), 561–566. CrossRefGoogle ScholarPubMed
Kittler, R., Pelletier, L., Heninger, A.K., Slabicki, M., Theis, M., Miroslaw, L., Poser, I., Lawo, S., Grabner, H., Kozak, K., Wagner, J., Surendranath, V., Richter, C., Bowen, W., Jackson, A.L., Habermann, B., Hyman, A.A., Buchholz, F.. Genome-scale RNAi profiling of cell division in human tissue culture cells. Nat Cell Biol., 9 (2007), No. 12, 1401–12. CrossRefGoogle ScholarPubMed
Pujana, M.A., Han, J.D., Starita, L.M., Stevens, K.N., Tewari, M., Ahn, J.S., Rennert, G., Moreno, V., Kirchhoff, T., Gold, B., Assmann, V., Elshamy, W.M., Rual, J.F., Levine, D., Rozek, L.S., Gelman, R.S., Gunsalus, K.C., Greenberg, R.A., Sobhian, B., Bertin, N., Venkatesan, K., Ayivi-Guedehoussou, N., Sole, X., Hernindez, P., Lazaro, C., Nathanson, K.L., Weber, B.L., Cusick, M.E., Hill, D.E., Offit, K., Livingston, D.M., Gruber, S.B., Parvin, J.D., Vidal, M.. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet., 39 (2007), No. 11, 1338–49. CrossRefGoogle ScholarPubMed
Vidal, M.. A biological atlas of functional maps. Cell, 9 (2001), No. 104(3), 333–339. CrossRefGoogle Scholar
Segal, E., Friedman, N., Koller, D., Regev, A.. A module map showing conditional activity of expression modules in cancer. Nat Genet., 36 (2004), No. 10, 1090–1098. CrossRefGoogle Scholar
Beyer, A., Bandyopadhyay, S., Ideker, T.. Integrating physical and genetic maps : from genomes to interaction networks. Nat Rev Genet., 8 (2007), No. 9, 699–710. CrossRefGoogle ScholarPubMed
Haberichter, T., Mädge, B., Christopher, R.A., Yoshioka, N., Dhiman, A., Miller, R., Gendelman, R., Aksenov, S.V., Khalil, I.G., Dowdy, S.F.. A systems biology dynamical model of mammalian G1 cell cycle progression. Mol Syst Biol., 3 (2007), No. 84. CrossRefGoogle ScholarPubMed
Sahin, O., Löbke, C., Korf, U., Appelhans, H., H, Sültmann, A, Poustka, S, Wiemann, D, Arlt. Combinatorial RNAi for quantitative protein network analysis. Proc Natl Acad Sci U S A., 17 (2007) No. 104(16), 6579–84. CrossRefGoogle Scholar
Bankhead, A. 3rd, Sach, I., Ni, C., LeMeur, N., Kruger, M., Ferrer, M., Gentleman, R., Rohl, C. Knowledge based identification of essential signaling from genome-scale siRNA experiments. BMC Syst Biol., 5 (2009), No. 3 :80. Google Scholar
Lehner, B., Crombie, C., Tischler, J., Fortunato, A., Fraser, A.G.. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat Genet., 38 (2006), No. 8, 896–903. CrossRefGoogle ScholarPubMed
Mukherji, M., Bell, R., Supekova, L., Wang, Y., Orth, A.P., Batalov, S., Miraglia, L., Huesken, D., Lange, J., Martin, C., Sahasrabudhe, S., Reinhardt, M., Natt, F., Hall, J., Mickanin, C., Labow, M., Chanda, S.K., Cho, C.Y., Schultz, P.G.. Genome-wide functional analysis of human cell-cycle regulators. PNAS 103 (2006), No. 40, 14819–14824. CrossRefGoogle ScholarPubMed
M.H. Beers, Lung Carcinoma. In The Merck manual of diagnosis and therapy (R.S. Porter, and T.V. Jones, editors). Rahway : Merck & Co., Inc. (2008), 2992.
Jemal, A., et al. Annual report to the nation on the status of cancer, 1975-2005, featuring trends in lung cancer, tobacco use, and tobacco control. J Natl Cancer Inst, 100 (2008), No. 23, 1672–1694. CrossRefGoogle ScholarPubMed
Kancha, R.K., von Bubnoff, N., Peschel, C., Duyster, J. Functional analysis of epidermal growth factor receptor (EGFR) mutations and potential implications for EGFR targeted therapy. Clin Cancer Res., 15 (2009), No. 2, 460–467. CrossRefGoogle ScholarPubMed
Miller, C.T., Chen, G., Gharib, T.G., Wang, H., Thomas, D.G., Misek, D.E., Giordano, T.J., Yee, J., Orringer, M.B., Hanash, S.M., Beer, D.G.. Increased C-CRK proto-oncogene expression is associated with an aggressive phenotype in lung adenocarcinomas. Oncogene 22 (2003), No. 39, 7950–7957. CrossRefGoogle ScholarPubMed
Zinovyev, A., Viara, E., Calzone, L., Barillot, E.. BiNoM : a Cytoscape plugin for manipulating and analyzing biological networks. Bioinformatics, 24 (2008), No. 6, 876–877. CrossRefGoogle ScholarPubMed
Shigematsu, H., Gazdar, A.F. Somatic mutations of epidermal growth factor receptor signaling pathway in lung cancers. Int J Cancer, 118 (2006), No. 2, 257–262. CrossRefGoogle ScholarPubMed
Mascaux, C., Iannino, N., Martin, B., Paesmans, M., Berghmans, T., Dusart, M., Haller, A., Lothaire, P., Meert, A.P., Noel, S., Lafitte, J.J., Sculier, J.P.. The role of RAS oncogene in survival of patients with lung cancer : a systematic review of the literature with meta-analysis. Br J Cancer., 92 (2005), No. 1, 131–139. CrossRefGoogle ScholarPubMed
Smoot, M., Ono, K., Ruscheinski, J., Wang, P.-L., Ideker, T.. Cytoscape 2.8 : new features for data integration and network visualization. Bioinformatics, 27 (2011), No. 3, 431–432. CrossRefGoogle ScholarPubMed
Chook, Y.M., Blobel, G.. Karyopherins and nuclear import. Curr Opin Struct Biol., 11 (2001), No. 6, 703–715. CrossRefGoogle ScholarPubMed
Lü, B., Xu, J., Zhu, Y., Zhang, H., Lai, M.. Systemic analysis of the differential gene expression profile in a colonic adenoma-normal SSH library. Clin Chim Acta., 378 (2007), No. 1-2, 42–47. CrossRefGoogle Scholar
Park, J.W., Bae, Y.S.. Phosphorylation of ribosomal protein L5 by protein kinase CKII decreases its 5S rRNA binding activity. Biochem Biophys Res Commun. 263 (1999), No. 2, 475–481. CrossRefGoogle ScholarPubMed