Literature DB >> 20179207

Signal transduction networks in cancer: quantitative parameters influence network topology.

David J Klinke1.   

Abstract

Networks of fixed topology are used to summarize the collective understanding of the flow of signaling information within a cell (i.e., canonical signaling networks). Moreover, these canonical signaling networks are used to interpret how observed oncogenic changes in protein activity or expression alter information flow in cancer cells. However, creating a novel branch within a signaling network (i.e., a noncanonical edge) provides a mechanism for a cell to acquire the hallmark characteristics of cancer. The objective of this study was to assess the existence of a noncanonical edge within a receptor tyrosine kinase (RTK) signaling network based upon variation in protein expression alone, using a mathematical model of the early signaling events associated with epidermal growth factor receptor 1 (ErbB1) signaling network as an illustrative example. The abundance of canonical protein-RTK complexes (e.g., growth factor receptor bound protein 2-ErbB1 and Src homology 2 domain containing transforming protein 1-ErbB1) were used to establish a threshold that was correlated with ligand-dependent changes in cell proliferation. Given the available data, the uncertainty associated with this threshold was estimated using an empirical Bayesian approach. Using the variability in protein expression observed among a collection of breast cancer cell lines, this model was used to assess whether a noncanonical edge (e.g., Irs1-ErbB1) exceeds the threshold and to identify cell lines where this noncanonical edge is likely to be observed. Taken together, the simulations suggest that the topology of signal transduction networks within cells is influenced by quantitative parameters, such as protein expression and binding affinity. Moreover, forming this noncanonical pathway was not due solely to overexpression of the cell surface receptor but was influenced by overexpression of all members of the multiprotein complex. Multivariate alterations in expression of signaling proteins in cancer cells may activate noncanonical pathways and may rewire the signaling network within a cell.

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Year:  2010        PMID: 20179207      PMCID: PMC2831142          DOI: 10.1158/0008-5472.CAN-09-3234

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  37 in total

1.  Robustness in simple biochemical networks.

Authors:  N Barkai; S Leibler
Journal:  Nature       Date:  1997-06-26       Impact factor: 49.962

2.  Quantification of information transfer via cellular signal transduction pathways.

Authors:  B N Kholodenko; J B Hoek; H V Westerhoff; G C Brown
Journal:  FEBS Lett       Date:  1997-09-08       Impact factor: 4.124

3.  Membrane targeting of the nucleotide exchange factor Sos is sufficient for activating the Ras signaling pathway.

Authors:  A Aronheim; D Engelberg; N Li; N al-Alawi; J Schlessinger; M Karin
Journal:  Cell       Date:  1994-09-23       Impact factor: 41.582

4.  Requirement for ras proto-oncogene function during serum-stimulated growth of NIH 3T3 cells.

Authors:  L S Mulcahy; M R Smith; D W Stacey
Journal:  Nature       Date:  1985 Jan 17-23       Impact factor: 49.962

5.  Dissociation of cytokine signals for proliferation and apoptosis.

Authors:  Y Shi; R Wang; A Sharma; C Gao; M Collins; L Penn; G B Mills
Journal:  J Immunol       Date:  1997-12-01       Impact factor: 5.422

6.  Quantification of short term signaling by the epidermal growth factor receptor.

Authors:  B N Kholodenko; O V Demin; G Moehren; J B Hoek
Journal:  J Biol Chem       Date:  1999-10-15       Impact factor: 5.157

7.  Accumulation of p21ras.GTP in response to stimulation with epidermal growth factor and oncogene products with tyrosine kinase activity.

Authors:  T Satoh; M Endo; M Nakafuku; T Akiyama; T Yamamoto; Y Kaziro
Journal:  Proc Natl Acad Sci U S A       Date:  1990-10       Impact factor: 11.205

8.  Ultrasensitivity in the mitogen-activated protein kinase cascade.

Authors:  C Y Huang; J E Ferrell
Journal:  Proc Natl Acad Sci U S A       Date:  1996-09-17       Impact factor: 11.205

9.  Genetic pathways to glioblastoma: a population-based study.

Authors:  Hiroko Ohgaki; Pierre Dessen; Benjamin Jourde; Sonja Horstmann; Tomofumi Nishikawa; Pier-Luigi Di Patre; Christoph Burkhard; Danielle Schüler; Nicole M Probst-Hensch; Paulo César Maiorka; Nathalie Baeza; Paola Pisani; Yasuhiro Yonekawa; M Gazi Yasargil; Urs M Lütolf; Paul Kleihues
Journal:  Cancer Res       Date:  2004-10-01       Impact factor: 12.701

10.  Receptor-mediated effects on ligand availability influence relative mitogenic potencies of epidermal growth factor and transforming growth factor alpha.

Authors:  C C Reddy; A Wells; D A Lauffenburger
Journal:  J Cell Physiol       Date:  1996-03       Impact factor: 6.384

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