Analysis of the Growth Control Network Specific for Human Lung Adenocarcinoma Cells

G. Pinna; A. Zinovyev; N. Araujo; N. Morozova; A. Harel-Bellan

Mathematical Modelling of Natural Phenomena (2012)

  • Volume: 7, Issue: 1, page 337-368
  • ISSN: 0973-5348

Abstract

top
Many cancer-associated genes and pathways remain to be identified in order to clarify the molecular mechanisms underlying cancer progression. In this area, genome-wide loss-of-function screens appear to be powerful biological tools, allowing the accumulation of large amounts of data. However, this approach currently lacks analytical tools to exploit the data with maximum efficiency, for which systems biology methods analyzing complex cellular networks may be extremely helpful. In this article we report such a systems biology strategy based on the construction of a Network for a biological process and specific for a given cell system (cell type). The networks are created from genome-wide loss-of-function screen datasets. We also propose tools to analyze network properties. As one of the tools, we suggest a mathematical model for discrimination between two distinct cell processes that may be affected by knocking down the activity of a gene, i. e., a decreased cell number may be caused by arrested cell proliferation or enhanced cell death. Next we show how this discrimination between the two cell processes helps to construct two corresponding subnetworks. Finally, we demonstrate an application of the proposed strategy to the identification and characterization of putative novel genes and pathways significant for the control of lung cancer cell growth, based on the results of a genome-wide proliferation/viability loss-of-function screen of human lung adenocarcinoma cells.

How to cite

top

Pinna, G., et al. "Analysis of the Growth Control Network Specific for Human Lung Adenocarcinoma Cells." Mathematical Modelling of Natural Phenomena 7.1 (2012): 337-368. <http://eudml.org/doc/222199>.

@article{Pinna2012,
abstract = {Many cancer-associated genes and pathways remain to be identified in order to clarify the molecular mechanisms underlying cancer progression. In this area, genome-wide loss-of-function screens appear to be powerful biological tools, allowing the accumulation of large amounts of data. However, this approach currently lacks analytical tools to exploit the data with maximum efficiency, for which systems biology methods analyzing complex cellular networks may be extremely helpful. In this article we report such a systems biology strategy based on the construction of a Network for a biological process and specific for a given cell system (cell type). The networks are created from genome-wide loss-of-function screen datasets. We also propose tools to analyze network properties. As one of the tools, we suggest a mathematical model for discrimination between two distinct cell processes that may be affected by knocking down the activity of a gene, i. e., a decreased cell number may be caused by arrested cell proliferation or enhanced cell death. Next we show how this discrimination between the two cell processes helps to construct two corresponding subnetworks. Finally, we demonstrate an application of the proposed strategy to the identification and characterization of putative novel genes and pathways significant for the control of lung cancer cell growth, based on the results of a genome-wide proliferation/viability loss-of-function screen of human lung adenocarcinoma cells.},
author = {Pinna, G., Zinovyev, A., Araujo, N., Morozova, N., Harel-Bellan, A.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {systems biology; network analysis; lung adenocarcinoma; genome-wide screen},
language = {eng},
month = {1},
number = {1},
pages = {337-368},
publisher = {EDP Sciences},
title = {Analysis of the Growth Control Network Specific for Human Lung Adenocarcinoma Cells},
url = {http://eudml.org/doc/222199},
volume = {7},
year = {2012},
}

TY - JOUR
AU - Pinna, G.
AU - Zinovyev, A.
AU - Araujo, N.
AU - Morozova, N.
AU - Harel-Bellan, A.
TI - Analysis of the Growth Control Network Specific for Human Lung Adenocarcinoma Cells
JO - Mathematical Modelling of Natural Phenomena
DA - 2012/1//
PB - EDP Sciences
VL - 7
IS - 1
SP - 337
EP - 368
AB - Many cancer-associated genes and pathways remain to be identified in order to clarify the molecular mechanisms underlying cancer progression. In this area, genome-wide loss-of-function screens appear to be powerful biological tools, allowing the accumulation of large amounts of data. However, this approach currently lacks analytical tools to exploit the data with maximum efficiency, for which systems biology methods analyzing complex cellular networks may be extremely helpful. In this article we report such a systems biology strategy based on the construction of a Network for a biological process and specific for a given cell system (cell type). The networks are created from genome-wide loss-of-function screen datasets. We also propose tools to analyze network properties. As one of the tools, we suggest a mathematical model for discrimination between two distinct cell processes that may be affected by knocking down the activity of a gene, i. e., a decreased cell number may be caused by arrested cell proliferation or enhanced cell death. Next we show how this discrimination between the two cell processes helps to construct two corresponding subnetworks. Finally, we demonstrate an application of the proposed strategy to the identification and characterization of putative novel genes and pathways significant for the control of lung cancer cell growth, based on the results of a genome-wide proliferation/viability loss-of-function screen of human lung adenocarcinoma cells.
LA - eng
KW - systems biology; network analysis; lung adenocarcinoma; genome-wide screen
UR - http://eudml.org/doc/222199
ER -

References

top
  1. C.J. Creighton, J.L. Bromberg-White, D.E. Misek, D.J. Monsma, F. Brichory, R. Kuick, T.J. Giordano, W. Gao, G.S. Omenn, C.P. Webb, S.M. Hanash. 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.  
  2. Y. Murakami. Functional cloning of a tumor suppressor gene, TSLC1, in human non-small cell lung cancer. Oncogene, 7 (2002), No. 21(45), 6936–48.  
  3. Y. Jiang, L. Cui, T.A. Yie, W.N. Rom, H. Cheng, K.M. Tchou-Wong. Inhibition of anchorage-independent growth and lung metastasis of A549 lung carcinoma cells by IkappaBbeta. Oncogene, 26 (2001), No. 20(18), 2254–63.  
  4. M. Soda, et al.Identification of the transforming EML4-ALK fusion gene in non-small cell lung cancer. Nature, 448 (2007), 561–566.  
  5. R. Kittler, L. Pelletier, A.K. Heninger, M. Slabicki, M. Theis, L. Miroslaw, I. Poser, S. Lawo, H. Grabner, K. Kozak, J. Wagner, V. Surendranath, C. Richter, W. Bowen, A.L. Jackson, B. Habermann, A.A. Hyman, F. Buchholz. Genome-scale RNAi profiling of cell division in human tissue culture cells. Nat Cell Biol., 9 (2007), No. 12, 1401–12.  
  6. M.A. Pujana, J.D. Han, L.M. Starita, K.N. Stevens, M. Tewari, J.S. Ahn, G. Rennert, V. Moreno, T. Kirchhoff, B. Gold, V. Assmann, W.M. Elshamy, J.F. Rual, D. Levine, L.S. Rozek, R.S. Gelman, K.C. Gunsalus, R.A. Greenberg, B. Sobhian, N. Bertin, K. Venkatesan, N. Ayivi-Guedehoussou, X. Sole, P. Hernindez, C. Lazaro, K.L. Nathanson, B.L. Weber, M.E. Cusick, D.E. Hill, K. Offit, D.M. Livingston, S.B. Gruber, J.D. Parvin, M. Vidal. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet., 39 (2007), No. 11, 1338–49.  
  7. M. Vidal. A biological atlas of functional maps. Cell, 9 (2001), No. 104(3), 333–339.  
  8. E. Segal, N. Friedman, D. Koller, A. Regev. A module map showing conditional activity of expression modules in cancer. Nat Genet., 36 (2004), No. 10, 1090–1098.  
  9. A. Beyer, S. Bandyopadhyay, T. Ideker. Integrating physical and genetic maps : from genomes to interaction networks. Nat Rev Genet., 8 (2007), No. 9, 699–710.  
  10. T. Haberichter, B. Mädge, R.A. Christopher, N. Yoshioka, A. Dhiman, R. Miller, R. Gendelman, S.V. Aksenov, I.G. Khalil, S.F. Dowdy. A systems biology dynamical model of mammalian G1 cell cycle progression. Mol Syst Biol., 3 (2007), No. 84.  
  11. O. Sahin, C. Löbke, U. Korf, H. Appelhans, Sültmann H, Poustka A, Wiemann S, Arlt D. Combinatorial RNAi for quantitative protein network analysis. Proc Natl Acad Sci U S A., 17 (2007) No. 104(16), 6579–84.  
  12. A.3rd Bankhead, I. Sach, C. Ni, N. LeMeur, M. Kruger, M. Ferrer, R. Gentleman, C. RohlKnowledge based identification of essential signaling from genome-scale siRNA experiments. BMC Syst Biol., 5 (2009), No. 3 :80.  
  13. B. Lehner, C. Crombie, J. Tischler, A. Fortunato, A.G. Fraser. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat Genet., 38 (2006), No. 8, 896–903.  
  14. M. Mukherji, R. Bell, L. Supekova, Y. Wang, A.P. Orth, S. Batalov, L. Miraglia, D. Huesken, J. Lange, C. Martin, S. Sahasrabudhe, M. Reinhardt, F. Natt, J. Hall, C. Mickanin, M. Labow, S.K. Chanda, C.Y. Cho, P.G. Schultz. Genome-wide functional analysis of human cell-cycle regulators. PNAS103 (2006), No. 40, 14819–14824.  
  15. 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.  
  16. A. Jemal, 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.  
  17. R.K. Kancha, N. von Bubnoff, C. Peschel, J. DuysterFunctional analysis of epidermal growth factor receptor (EGFR) mutations and potential implications for EGFR targeted therapy. Clin Cancer Res., 15 (2009), No. 2, 460–467.  
  18. C.T. Miller, G. Chen, T.G. Gharib, H. Wang, D.G. Thomas, D.E. Misek, T.J. Giordano, J. Yee, M.B. Orringer, S.M. Hanash, D.G. Beer. Increased C-CRK proto-oncogene expression is associated with an aggressive phenotype in lung adenocarcinomas. Oncogene22 (2003), No. 39, 7950–7957.  
  19. A. Zinovyev, E. Viara, L. Calzone, E. Barillot. BiNoM : a Cytoscape plugin for manipulating and analyzing biological networks. Bioinformatics, 24 (2008), No. 6, 876–877.  
  20. H. Shigematsu, A.F. GazdarSomatic mutations of epidermal growth factor receptor signaling pathway in lung cancers. Int J Cancer, 118 (2006), No. 2, 257–262.  
  21. C. Mascaux, N. Iannino, B. Martin, M. Paesmans, T. Berghmans, M. Dusart, A. Haller, P. Lothaire, A.P. Meert, S. Noel, J.J. Lafitte, J.P. Sculier. 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.  
  22. M. Smoot, K. Ono, J. Ruscheinski, P.-L. Wang, T. Ideker. Cytoscape 2.8 : new features for data integration and network visualization. Bioinformatics, 27 (2011), No. 3, 431–432.  
  23. Y.M. Chook, G. Blobel. Karyopherins and nuclear import. Curr Opin Struct Biol., 11 (2001), No. 6, 703–715.  
  24. B. Lü, J. Xu, Y. Zhu, H. Zhang, M. Lai. 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.  
  25. J.W. Park, Y.S. Bae. 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.  

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.