Analysis of cancer signaling networks by systems biology to develop therapies

Seminars in Cancer Biology

Volumn 21, Issue 3, 2011, Pages 200-206

  Prasasya, Rexxi D ^a   Tian, Dan ^a   Kreeger, Pamela K ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Cancer; Computational model; Molecular targeted therapy; Systems biology

Indexed keywords

4 DIALLYLAMINOMETHYLENE 1,3,4,7,10,11,12,13,14,15,16,17 DODECAHYDRO 6 HYDROXY 1 METHOXYMETHYL 10,13 DIMETHYL 3,7,17 TRIOXO 2 OXACYCLOPENTA[A]PHENANTHREN 11 YL ACETATE; ANTINEOPLASTIC AGENT; BETACELLULIN; CCAAT ENHANCER BINDING PROTEIN BETA; CHLORPROMAZINE; CYCLIN D1; CYCLIN DEPENDENT KINASE 4; EPIDERMAL GROWTH FACTOR; EPIDERMAL GROWTH FACTOR RECEPTOR; EPIDERMAL GROWTH FACTOR RECEPTOR 2; EPIDERMAL GROWTH FACTOR RECEPTOR 3; EPIDERMAL GROWTH FACTOR RECEPTOR 4; ESTROGEN RECEPTOR ALPHA; MITOGEN ACTIVATED PROTEIN KINASE; MITOGEN ACTIVATED PROTEIN KINASE P38; MM 121; MONOCLONAL ANTIBODY; MYC PROTEIN; NERVE GROWTH FACTOR; NEU DIFFERENTIATION FACTOR; PENTAMIDINE; PHOSPHATIDYLINOSITOL 3 KINASE; PHOSPHATIDYLINOSITOL 3 KINASE INHIBITOR; PROTEIN KINASE B; PROTEIN TYROSINE KINASE; RAS PROTEIN; STAT3 PROTEIN; TANESPIMYCIN; TRASTUZUMAB; UNCLASSIFIED DRUG; UNINDEXED DRUG;

B CELL LYMPHOMA; BREAST CANCER; CANCER INHIBITION; CARCINOGENESIS; CELL FUNCTION; DRUG RESPONSE; DRUG SENSITIVITY; DRUG SPECIFICITY; DRUG TARGETING; FOLLICULAR LYMPHOMA; GENE EXPRESSION; GENE MUTATION; GLIOBLASTOMA; GLIOMA; HUMAN; IC 50; LUNG CANCER; LUNG TUMOR; NEOPLASM; NONHUMAN; PROTEIN DNA INTERACTION; PROTEIN EXPRESSION; PROTEIN PROTEIN INTERACTION; REVIEW; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

HUMANS; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 79960741376     PISSN: 1044579X     EISSN: 10963650     Source Type: Journal    
DOI: 10.1016/j.semcancer.2011.04.001     Document Type: Review

References (89)

1. Wood L.D., Parsons D.W., Jones S., Lin J., Sjoblom T., Leary R.J., et al. The genomic landscapes of human breast and colorectal cancers. Science 2007, 318:1108-1113.
2. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008, 455:1061-1068. The Cancer Genome Atlas Research Network.
3. Leary R.J., Lin J.C., Cummins J., Boca S., Wood L.D., Parsons D.W., et al. Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers. Proc Natl Acad Sci USA 2008, 105:16224-16229.
4. Fox E.J., Salk J.J., Loeb L.A. Cancer genome sequencing-an interim analysis. Cancer Res 2009, 69:4948-4950.
5. Weinstein I.B., Joe A. Oncogene addiction. Cancer Res 2008, 68:3077-3080. discussion 80.
6. Cerami E., Demir E., Schultz N., Taylor B.S., Sander C. Automated network analysis identifies core pathways in glioblastoma. PLoS One 2010, 5:e8918.
7. Pawson T., Warner N. Oncogenic re-wiring of cellular signaling pathways. Oncogene 2007, 26:1268-1275.
8. Pawson T., Linding R. Network medicine. FEBS Lett 2008, 582:1266-1270.
9. Kreeger P.K., Lauffenburger D.A. Cancer systems biology: a network modeling perspective. Carcinogenesis 2010, 31:2-8.
10. Hornberg J.J., Bruggeman F.J., Westerhoff H.V., Lankelma J. Cancer: a systems biology disease. Biosystems 2006, 83:81-90.
11. Anderson A.R., Quaranta V. Integrative mathematical oncology. Nat Rev Cancer 2008, 8:227-234.
12. Janes K.A., Yaffe M.B. Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006, 7:820-828.
13. Morris M.K., Saez-Rodriguez J., Sorger P.K., Lauffenburger D.A. Logic-based models for the analysis of cell signaling networks. Biochemistry 2010, 49:3216-3224.
14. Aldridge B.B., Burke J.M., Lauffenburger D.A., Sorger P.K. Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006, 8:1195-1203.
15. Gingras A.C., Gstaiger M., Raught B., Aebersold R. Analysis of protein complexes using mass spectrometry. Nat Rev Mol Cell Biol 2007, 8:645-654.
16. Higurashi M., Ishida T., Kinoshita K. PiSite: a database of protein interaction sites using multiple binding states in the PDB. Nucleic Acids Res 2009, 37:D360-D364.
17. von Kriegsheim A., Baiocchi D., Birtwistle M., Sumpton D., Bienvenut W., Morrice N., et al. Cell fate decisions are specified by the dynamic ERK interactome. Nat Cell Biol 2009, 11:1458-1464.
18. Mani K.M., Lefebvre C., Wang K., Lim W.K., Basso K., Dalla-Favera R., et al. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol Syst Biol 2008, 4:169.
19. Chen J., Sam L., Huang Y., Lee Y., Li J., Liu Y., et al. Protein interaction network underpins concordant prognosis among heterogeneous breast cancer signatures. J Biomed Inform 2010, 43:385-396.
20. Carro M.S., Lim W.K., Alvarez M.J., Bollo R.J., Zhao X., Snyder E.Y., et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 2010, 463:318-325.
21. Linding R., Jensen L.J., Ostheimer G.J., van Vugt M.A., Jorgensen C., Miron I.M., et al. Systematic discovery of in vivo phosphorylation networks. Cell 2007, 129:1415-1426.
22. Ma'ayan A., Jenkins S.L., Neves S., Hasseldine A., Grace E., Dubin-Thaler B., et al. Formation of regulatory patterns during signal propagation in a Mammalian cellular network. Science 2005, 309:1078-1083.
23. Zhou D., He Y. Extracting interactions between proteins from the literature. J Biomed Inform 2008, 41:393-407.
24. Apgar J.F., Toettcher J.E., Endy D., White F.M., Tidor B. Stimulus design for model selection and validation in cell signaling. PLoS Comput Biol 2008, 4:e30.
25. Apgar J.F., Witmer D.K., White F.M., Tidor B. Sloppy models, parameter uncertainty, and the role of experimental design. Mol Biosyst 2010, 6:1890-1900.
26. Bruggeman F.J., Westerhoff H.V., Hoek J.B., Kholodenko B.N. Modular response analysis of cellular regulatory networks. J Theor Biol 2002, 218:507-520.
27. Santos S.D., Verveer P.J., Bastiaens P.I. Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate. Nat Cell Biol 2007, 9:324-330.
28. Mitsos A., Melas I.N., Siminelakis P., Chairakaki A.D., Saez-Rodriguez J., Alexopoulos L.G. Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput Biol 2009, 5:e1000591.
29. Woolf P.J., Prudhomme W., Daheron L., Daley G.Q., Lauffenburger D.A. Bayesian analysis of signaling networks governing embryonic stem cell fate decisions. Bioinformatics 2005, 21:741-753.
30. Sachs K., Perez O., Pe'er D., Lauffenburger D.A., Nolan G.P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005, 308:523-529.
31. Sachs K., Gentles A.J., Youland R., Itani S., Irish J., Nolan G.P., et al. Characterization of patient specific signaling via augmentation of Bayesian networks with disease and patient state nodes. Conf Proc IEEE Eng Med Biol Soc 2009, 2009:6624-6627.
32. Yarden Y., Sliwkowski M.X. Untangling the ErbB signalling network. Nat Rev Mol Cell Biol 2001, 2:127-137.
33. Hynes N.E., Lane H.A. ERBB receptors and cancer: the complexity of targeted inhibitors. Nat Rev Cancer 2005, 5:341-354.
34. Lazzara M.J., Lauffenburger D.A. Quantitative modeling perspectives on the ErbB system of cell regulatory processes. Exp Cell Res 2009, 315:717-725.
35. Hendriks B.S., Griffiths G.J., Benson R., Kenyon D., Lazzara M., Swinton J., et al. Decreased internalisation of erbB1 mutants in lung cancer is linked with a mechanism conferring sensitivity to gefitinib. Syst Biol (Stevenage) 2006, 153:457-466.
36. Orton R.J., Adriaens M.E., Gormand A., Sturm O.E., Kolch W., Gilbert D.R. Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway. BMC Syst Biol 2009, 3:100.
37. Hendriks B.S., Orr G., Wells A., Wiley H.S., Lauffenburger D.A., Parsing ERK activation reveals quantitatively equivalent contributions from epidermal growth factor receptor and HER2 in human mammary epithelial cells. J Biol Chem 2005, 280:6157-6169.
38. Birtwistle M.R., Hatakeyama M., Yumoto N., Ogunnaike B.A., Hoek J.B., Kholodenko B.N. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol 2007, 3:144.
39. Chen W.W., Schoeberl B., Jasper P.J., Niepel M., Nielsen U.B., Lauffenburger D.A., et al. Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol Syst Biol 2009, 5:239.
40. Wang D.Y., Cardelli L., Phillips A., Piterman N., Fisher J. Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics. BMC Syst Biol 2009, 3:118.
41. Kreeger P.K., Mandhana R., Alford S.K., Haigis K.M., Lauffenburger D.A. RAS mutations affect tumor necrosis factor-induced apoptosis in colon carcinoma cells via ERK-modulatory negative and positive feedback circuits along with non-ERK pathway effects. Cancer Res 2009, 69:8191-8199.
42. Kumar N., Wolf-Yadlin A., White F.M., Lauffenburger D.A. Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine data. PLoS Comput Biol 2007, 3:e4.
43. Athale C., Mansury Y., Deisboeck T.S. Simulating the impact of a molecular 'decision-process' on cellular phenotype and multicellular patterns in brain tumors. J Theor Biol 2005, 233:469-481.
44. Athale C.A., Deisboeck T.S. The effects of EGF-receptor density on multiscale tumor growth patterns. J Theor Biol 2006, 238:771-779.
45. Zhang L., Athale C.A., Deisboeck T.S. Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. J Theor Biol 2007, 244:96-107.
46. Zhang L., Strouthos C.G., Wang Z., Deisboeck T.S. Simulating brain tumor heterogeneity with a multiscale agent-based model: linking molecular signatures, phenotypes and expansion rate. Math Comput Model 2009, 49:307-319.
47. Frame M.C. Src in cancer: deregulation and consequences for cell behaviour. Biochim Biophys Acta 2002, 1602:114-130.
48. Guarino M. Src signaling in cancer invasion. J Cell Physiol 2010, 223:14-26.
49. Saad F., Lipton A. SRC kinase inhibition: targeting bone metastases and tumor growth in prostate and breast cancer. Cancer Treat Rev 2010, 36:177-184.
50. Fuss H., Dubitzky W., Downes C.S., Kurth M.J. SRC family kinases and receptors: analysis of three activation mechanisms by dynamic systems modeling. Biophys J 2008, 94:1995-2006.
51. Kaimachnikov N.P., Kholodenko B.N. Toggle switches, pulses and oscillations are intrinsic properties of the Src activation/deactivation cycle. FEBS J 2009, 276:4102-4118.
52. Ramis-Conde I., Chaplain M.A., Anderson A.R., Drasdo D. Multi-scale modelling of cancer cell intravasation: the role of cadherins in metastasis. Phys Biol 2009, 6:016008.
53. Borisov N., Aksamitiene E., Kiyatkin A., Legewie S., Berkhout J., Maiwald T., et al. Systems-level interactions between insulin-EGF networks amplify mitogenic signaling. Mol Syst Biol 2009, 5:256.
54. Heiser L.M., Wang N.J., Talcott C.L., Laderoute K.R., Knapp M., Guan Y., et al. Integrated analysis of breast cancer cell lines reveals unique signaling pathways. Genome Biol 2009, 10:R31.
55. Dillon R.L., White D.E., Muller W.J. The phosphatidyl inositol 3-kinase signaling network: implications for human breast cancer. Oncogene 2007, 26:1338-1345.
56. Marone R., Cmiljanovic V., Giese B., Wymann M.P. Targeting phosphoinositide 3-kinase: moving towards therapy. Biochim Biophys Acta 2008, 1784:159-185.
57. Jiang B.H., Liu L.Z. PI3K/PTEN signaling in tumorigenesis and angiogenesis. Biochim Biophys Acta 2008, 1784:150-158.
58. Ihle N.T., Lemos R., Wipf P., Yacoub A., Mitchell C., Siwak D., et al. Mutations in the phosphatidylinositol-3-kinase pathway predict for antitumor activity of the inhibitor PX-866 whereas oncogenic Ras is a dominant predictor for resistance. Cancer Res 2009, 69:143-150.
59. Dan S., Okamura M., Seki M., Yamazaki K., Sugita H., Okui M., et al. Correlating phosphatidylinositol 3-kinase inhibitor efficacy with signaling pathway status: in silico and biological evaluations. Cancer Res 2010, 70:4982-4994.
60. Monine M.I., Haugh J.M. Signal transduction at point-blank range: analysis of a spatial coupling mechanism for pathway crosstalk. Biophys J 2008, 95:2172-2182.
61. Wang C.C., Cirit M., Haugh J.M. PI3K-dependent cross-talk interactions converge with Ras as quantifiable inputs integrated by Erk. Mol Syst Biol 2009, 5:246.
62. Samaga R., Saez-Rodriguez J., Alexopoulos L.G., Sorger P.K., Klamt S. The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput Biol 2009, 5:e1000438.
63. Schoeberl B., Pace E.A., Fitzgerald J.B., Harms B.D., Xu L., Nie L., et al. Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2009, 2:ra31.
64. Mukai H. Targeted therapy in breast cancer: current status and future directions. Jpn J Clin Oncol 2010, 40:711-716.
65. Romond E.H., Perez E.A., Bryant J., Suman V.J., Geyer C.E., Davidson N.E., et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005, 353:1673-1684.
66. Piccart-Gebhart M.J., Procter M., Leyland-Jones B., Goldhirsch A., Untch M., Smith I., et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med 2005, 353:1659-1672.
67. Sahin O., Frohlich H., Lobke C., Korf U., Burmester S., Majety M., et al. Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance. BMC Syst Biol 2009, 3:1.
68. Faratian D., Goltsov A., Lebedeva G., Sorokin A., Moodie S., Mullen P., et al. Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab. Cancer Res 2009, 69:6713-6720.
69. Kreeger P.K., Wang Y., Haigis K.M., Lauffenburger D.A. Integration of multiple signaling pathway activities resolves K-RAS/N-RAS mutation paradox in colon epithelial cell response to inflammatory cytokine stimulation. Integr Biol (Camb) 2010, 2:202-208.
70. Abou-Jawde R., Choueiri T., Alemany C., Mekhail T. An overview of targeted treatments in cancer. Clin Ther 2003, 25:2121-2137.
71. Weinstein I.B., Joe A.K. Mechanisms of disease: oncogene addiction-a rationale for molecular targeting in cancer therapy. Nat Clin Pract Oncol 2006, 3:448-457.
72. Cleator S.J., Ahamed E., Coombes R.C., Palmieri C. A 2009 update on the treatment of patients with hormone receptor-positive breast cancer. Clin Breast Cancer 2009, 9(Suppl. 1):S6-S17.
73. Hirsch F.R., Varella-Garcia M., Cappuzzo F. Predictive value of EGFR and HER2 overexpression in advanced non-small-cell lung cancer. Oncogene 2009, 28(Suppl. 1):S32-S37.
74. Jiang H., Reinhardt H.C., Bartkova J., Tommiska J., Blomqvist C., Nevanlinna H., et al. The combined status of ATM and p53 link tumor development with therapeutic response. Genes Dev 2009, 23:1895-1909.
75. Schoeberl B., Faber A.C., Li D., Liang M.C., Crosby K., Onsum M., et al. An ErbB3 antibody, MM-121, is active in cancers with ligand-dependent activation. Cancer Res 2010, 70:2485-2494.
76. Pritchard J.R., Cosgrove B.D., Hemann M.T., Griffith L.G., Wands J.R., Lauffenburger D.A. Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death. Mol Cancer Ther 2009, 8:2183-2192.
77. Kumar N., Afeyan R., Kim H.D., Lauffenburger D.A. Multipathway model enables prediction of kinase inhibitor cross-talk effects on migration of Her2-overexpressing mammary epithelial cells. Mol Pharmacol 2008, 73:1668-1678.
78. Chang H.Y., Nuyten D.S., Sneddon J.B., Hastie T., Tibshirani R., Sorlie T., et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA 2005, 102:3738-3743.
79. Paik S., Shak S., Tang G., Kim C., Baker J., Cronin M., et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004, 351:2817-2826.
80. Verhaak R.G., Hoadley K.A., Purdom E., Wang V., Qi Y., Wilkerson M.D., et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010, 17:98-110.
81. Nevins J.R., Potti A. Mining gene expression profiles: expression signatures as cancer phenotypes. Nat Rev Genet 2007, 8:601-609.
82. Chang J.T., Carvalho C., Mori S., Bild A.H., Gatza M.L., Wang Q., et al. A genomic strategy to elucidate modules of oncogenic pathway signaling networks. Mol Cell 2009, 34:104-114.
83. Dancey J.E., Chen H.X. Strategies for optimizing combinations of molecularly targeted anticancer agents. Nat Rev Drug Discov 2006, 5:649-659.
84. Keith C.T., Borisy A.A., Stockwell B.R. Multicomponent therapeutics for networked systems. Nat Rev Drug Discov 2005, 4:71-78.
85. Iadevaia S., Lu Y., Morales F.C., Mills G.B., Ram P.T. Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res 2010.
86. Borisy A.A., Elliott P.J., Hurst N.W., Lee M.S., Lehar J., Price E.R., et al. Systematic discovery of multicomponent therapeutics. Proc Natl Acad Sci USA 2003, 100:7977-7982.
87. Vazquez A. Optimal drug combinations and minimal hitting sets. BMC Syst Biol 2009, 3:81.
88. Chatterjee M.S., Purvis J.E., Brass L.F., Diamond S.L. Pairwise agonist scanning predicts cellular signaling responses to combinatorial stimuli. Nat Biotechnol 2010, 28:727-732.
89. Yan H., Zhang B., Li S., Zhao Q. A formal model for analyzing drug combination effects and its application in TNF-alpha-induced NFkappaB pathway. BMC Syst Biol 2010, 4:50.