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The dynamic control of signal transduction networks in cancer cells

Key Points

  • Signal transduction networks (STNs) regulate fundamental biological processes by integrating and computing external and internal signals to adjust cellular physiology to changing environmental and intrinsic cues.

  • Much of the computing function of STNs is carried out by small network modules that function as switches, noise filters, oscillators, signal integrators and distributors, combinatorial ensembles of which can translate complex molecular reactions into accurate biological responses.

  • STN components are frequently affected in cancer by genetic mutations or epigenetic silencing, causing STN rewiring that modulates the biological effects of these genetic and epigenetic changes.

  • These STN alterations in cancer dynamically integrate intrinsic genetic and epigenetic changes with external stress factors, such as drug or radiation treatments, generating new network states that can profoundly change the ability of the cancer cells to survive, grow and spread.

  • Mathematical and computational modelling are powerful tools for the analysis of STNs and STN aberrations and for understanding and predicting the effects of mutations and therapeutic interventions in cancer.

Abstract

Cancer is often considered a genetic disease. However, much of the enormous plasticity of cancer cells to evolve different phenotypes, to adapt to challenging microenvironments and to withstand therapeutic assaults is encoded by the structure and spatiotemporal dynamics of signal transduction networks. In this Review, we discuss recent concepts concerning how the rich signalling dynamics afforded by these networks are regulated and how they impinge on cancer cell proliferation, survival, invasiveness and drug resistance. Understanding this dynamic circuitry by mathematical modelling could pave the way to new therapeutic approaches and personalized treatments.

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Figure 1: Examples of the role of feedback control in signal transduction networks.
Figure 2: Dynamic control of EMT.
Figure 3: Pathway crosstalk.
Figure 4: Transcriptional dynamics.
Figure 5: Network-mediated mechanisms of drug resistance.

References

  1. Klein, C. A. Selection and adaptation during metastatic cancer progression. Nature 501, 365–372 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Junttila, M. R. & de Sauvage, F. J. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 501, 346–354 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Wiener, N. Cybernetics, or Control and Communication in the Animal and the Machine (MIT Press, 1948).

    Google Scholar 

  4. Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. Kholodenko, B. N. Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165–176 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shoval, O. & Alon, U. SnapShot: network motifs. Cell 143, 326.e1 (2010).

    Article  CAS  Google Scholar 

  7. Ferrell, J. E. Jr. Feedback loops and reciprocal regulation: recurring motifs in the systems biology of the cell cycle. Curr. Opin. Cell Biol. 25, 676–686 (2013).

    Article  PubMed  CAS  Google Scholar 

  8. Fisher, D., Krasinska, L., Coudreuse, D. & Novak, B. Phosphorylation network dynamics in the control of cell cycle transitions. J. Cell Sci. 125, 4703–4711 (2012).

    Article  CAS  PubMed  Google Scholar 

  9. Burkhart, D. L. & Sage, J. Cellular mechanisms of tumour suppression by the retinoblastoma gene. Nat. Rev. Cancer 8, 671–682 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Yao, G., Lee, T. J., Mori, S., Nevins, J. R. & You, L. A bistable Rb–E2F switch underlies the restriction point. Nat. Cell Biol. 10, 476–482 (2008). A seminal investigation of R-point regulation combining experimental work, single-cell analysis and mathematical modelling.

    Article  CAS  PubMed  Google Scholar 

  11. Tyson, J. J. et al. Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells. Nat. Rev. Cancer 11, 523–532 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yao, G., Tan, C., West, M., Nevins, J. R. & You, L. Origin of bistability underlying mammalian cell cycle entry. Mol. Syst. Biol. 7, 485 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Blagosklonny, M. V. & Pardee, A. B. The restriction point of the cell cycle. Cell Cycle 1, 103–110 (2002).

    CAS  PubMed  Google Scholar 

  14. Dick, F. A. & Rubin, S. M. Molecular mechanisms underlying RB protein function. Nat. Rev. Mol. Cell Biol. 14, 297–306 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Conradie, R. et al. Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex. FEBS J. 277, 357–367 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Asghar, U., Witkiewicz, A. K., Turner, N. C. & Knudsen, E. S. The history and future of targeting cyclin-dependent kinases in cancer therapy. Nat. Rev. Drug Discov. 14, 130–146 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bonelli, P., Tuccillo, F. M., Borrelli, A., Schiattarella, A. & Buonaguro, F. M. CDK/CCN and CDKI alterations for cancer prognosis and therapeutic predictivity. Biomed. Res. Int. 2014, 361020 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lee, Y. M. & Sicinski, P. Targeting cyclins and cyclin-dependent kinases in cancer: lessons from mice, hopes for therapeutic applications in human. Cell Cycle 5, 2110–2114 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Zarkowska, T. & Mittnacht, S. Differential phosphorylation of the retinoblastoma protein by G1/S cyclin-dependent kinases. J. Biol. Chem. 272, 12738–12746 (1997).

    Article  CAS  PubMed  Google Scholar 

  20. Knudsen, E. S. & Knudsen, K. E. Tailoring to RB: tumour suppressor status and therapeutic response. Nat. Rev. Cancer 8, 714–724 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen, Y. N. et al. Selective killing of transformed cells by cyclin/cyclin-dependent kinase 2 antagonists. Proc. Natl Acad. Sci. USA 96, 4325–4329 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sharma, A. et al. Retinoblastoma tumor suppressor status is a critical determinant of therapeutic response in prostate cancer cells. Cancer Res. 67, 6192–6203 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sauro, H. M. & Kholodenko, B. N. Quantitative analysis of signaling networks. Prog. Biophys. Mol. Biol. 86, 5–43 (2004).

    Article  CAS  PubMed  Google Scholar 

  24. Sturm, O. E. et al. The mammalian MAPK/ERK pathway exhibits properties of a negative feedback amplifier. Sci. Signal. 3, ra90 (2010). This paper describes the existence and effects of the NFA, a well-known engineering device, in biological systems. For example, this paper predicted the counterintuitive synergy between RAF and MEK inhibitors.

    Article  CAS  PubMed  Google Scholar 

  25. Fritsche-Guenther, R. et al. Strong negative feedback from Erk to Raf confers robustness to MAPK signalling. Mol. Syst. Biol. 7, 489 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Albeck, J. G., Mills, G. B. & Brugge, J. S. Frequency-modulated pulses of ERK activity transmit quantitative proliferation signals. Mol. Cell 49, 249–261 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Sample, V., Mehta, S. & Zhang, J. Genetically encoded molecular probes to visualize and perturb signaling dynamics in living biological systems. J. Cell Sci. 127, 1151–1160 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Aoki, K., Kamioka, Y. & Matsuda, M. Fluorescence resonance energy transfer imaging of cell signaling from in vitro to in vivo: basis of biosensor construction, live imaging, and image processing. Dev. Growth Differ. 55, 515–522 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Dehmelt, L. & Bastiaens, P. I. Spatial organization of intracellular communication: insights from imaging. Nat. Rev. Mol. Cell Biol. 11, 440–452 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Kholodenko, B. N., Hoek, J. B. & Westerhoff, H. V. Why cytoplasmic signalling proteins should be recruited to cell membranes. Trends Cell Biol. 10, 173–178 (2000).

    Article  CAS  PubMed  Google Scholar 

  31. Zimmermann, G. et al. Small molecule inhibition of the KRAS–PDEδ interaction impairs oncogenic KRAS signalling. Nature 497, 638–642 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Iglesias, D. A. et al. Another surprise from metformin: novel mechanism of action via K-Ras influences endometrial cancer response to therapy. Mol. Cancer Ther. 12, 2847–2856 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Cho, K. J. et al. Raf inhibitors target Ras spatiotemporal dynamics. Curr. Biol. 22, 945–955 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Virchow, R. Die Cellularpathologie in ihrer Begründung auf physiologische und pathologische Gewebelehre (Berlin Verlag von August Hirschwald, 1858).

    Google Scholar 

  35. Van Loo, P. & Voet, T. Single cell analysis of cancer genomes. Curr. Opin. Genet. Dev. 24, 82–91 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Barber, L. J., Davies, M. N. & Gerlinger, M. Dissecting cancer evolution at the macro-heterogeneity and micro-heterogeneity scale. Curr. Opin. Genet. Dev. 30, 1–6 (2014).

    Article  PubMed  CAS  Google Scholar 

  37. Diaz, L. A. Jr et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Almendro, V. et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep. 6, 514–527 (2014). This is an insightful and comprehensive analysis of tumour evolution under the pressures of chemotherapy.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tarin, D. Role of the host stroma in cancer and its therapeutic significance. Cancer Metastasis Rev. 32, 553–566 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Pereira, E. R., Jones, D., Jung, K. & Padera, T. P. The lymph node microenvironment and its role in the progression of metastatic cancer. Semin. Cell Dev. Biol. 38, 98–105 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Irish, J. M. et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118, 217–228 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. Cohen, A. A. et al. Dynamic proteomics of individual cancer cells in response to a drug. Science 322, 1511–1516 (2008). This study uses real-time, single-cell imaging to track changes in protein abundances under drug treatment, revealing numerous and complex changes.

    Article  CAS  PubMed  Google Scholar 

  43. Dobrzynski, M. et al. Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses. J. R. Soc. Interface 11, 20140383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Koludrovic, D. & Davidson, I. MITF, the Janus transcription factor of melanoma. Future Oncol. 9, 235–244 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002).

    Article  CAS  PubMed  Google Scholar 

  46. Lister, J. A. et al. A conditional zebrafish MITF mutation reveals MITF levels are critical for melanoma promotion versus regression in vivo. J. Invest. Dermatol. 134, 133–140 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Flaherty, K. T., Hodi, F. S. & Fisher, D. E. From genes to drugs: targeted strategies for melanoma. Nat. Rev. Cancer 12, 349–361 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Carreira, S. et al. Mitf regulation of Dia1 controls melanoma proliferation and invasiveness. Genes Dev. 20, 3426–3439 (2006). This paper elegantly shows that MITF controls different biological programmes through differential effects on gene expression.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Strub, T. et al. Essential role of microphthalmia transcription factor for DNA replication, mitosis and genomic stability in melanoma. Oncogene 30, 2319–2332 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Cheli, Y. et al. Mitf is the key molecular switch between mouse or human melanoma initiating cells and their differentiated progeny. Oncogene 30, 2307–2318 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Du, J. et al. Critical role of CDK2 for melanoma growth linked to its melanocyte-specific transcriptional regulation by MITF. Cancer Cell 6, 565–576 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. Bentley, N. J., Eisen, T. & Goding, C. R. Melanocyte-specific expression of the human tyrosinase promoter: activation by the microphthalmia gene product and role of the initiator. Mol. Cell. Biol. 14, 7996–8006 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Hoek, K. S. et al. In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res. 68, 650–656 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Ennen, M. et al. Single-cell gene expression signatures reveal melanoma cell heterogeneity. Oncogene 34, 3251–3263 (2014).

    Article  PubMed  CAS  Google Scholar 

  55. Chapman, A. et al. Heterogeneous tumor subpopulations cooperate to drive invasion. Cell Rep. 8, 688–695 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Nieto, M. A. The ins and outs of the epithelial to mesenchymal transition in health and disease. Annu. Rev. Cell Dev. Biol. 27, 347–376 (2011).

    Article  CAS  PubMed  Google Scholar 

  57. Lamouille, S., Xu, J. & Derynck, R. Molecular mechanisms of epithelial–mesenchymal transition. Nat. Rev. Mol. Cell Biol. 15, 178–196 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shin, S. Y. et al. Functional roles of multiple feedback loops in extracellular signal-regulated kinase and Wnt signaling pathways that regulate epithelial–mesenchymal transition. Cancer Res. 70, 6715–6724 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Yeung, K. et al. Suppression of Raf-1 kinase activity and MAP kinase signalling by RKIP. Nature 401, 173–177 (1999).

    Article  CAS  PubMed  Google Scholar 

  60. Wu, K. & Bonavida, B. The activated NF-κB-Snail-RKIP circuitry in cancer regulates both the metastatic cascade and resistance to apoptosis by cytotoxic drugs. Crit. Rev. Immunol. 29, 241–254 (2009).

    Article  CAS  PubMed  Google Scholar 

  61. Yeung, K. C. et al. Raf kinase inhibitor protein interacts with NF-κB-inducing kinase and TAK1 and inhibits NF-κB activation. Mol. Cell. Biol. 21, 7207–7217 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Yesilkanal, A. E. & Rosner, M. R. Raf kinase inhibitory protein (RKIP) as a metastasis suppressor: regulation of signaling networks in cancer. Crit. Rev. Oncog. 19, 447–454 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Escara-Wilke, J., Yeung, K. & Keller, E. T. Raf kinase inhibitor protein (RKIP) in cancer. Cancer Metastasis Rev. 31, 615–620 (2012).

    Article  CAS  PubMed  Google Scholar 

  64. Lee, J. et al. Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions. Proc. Natl Acad. Sci. USA 111, E364–E373 (2014). This study uses biochemical experiments, single-cell investigations and mathematical modelling to unravel complex feedback regulation of transcriptional networks in the regulation of EMTs.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Zhang, J. et al. TGF-β-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci. Signal. 7, ra91 (2014).

    PubMed  Google Scholar 

  66. Lu, M., Jolly, M. K., Levine, H., Onuchic, J. N. & Ben-Jacob, E. MicroRNA-based regulation of epithelial–hybrid–mesenchymal fate determination. Proc. Natl Acad. Sci. USA 110, 18144–18149 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Huang, R. Y. et al. An EMT spectrum defines an anoikis-resistant and spheroidogenic intermediate mesenchymal state that is sensitive to e-cadherin restoration by a src-kinase inhibitor, saracatinib (AZD0530). Cell Death Dis. 4, e915 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hart, J. R. et al. The butterfly effect in cancer: a single base mutation can remodel the cell. Proc. Natl Acad. Sci. USA 112, 1131–1136 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wu, X. et al. Activation of diverse signalling pathways by oncogenic PIK3CA mutations. Nat. Commun. 5, 4961 (2014).

    Article  CAS  PubMed  Google Scholar 

  70. Tomasetti, C., Marchionni, L., Nowak, M. A., Parmigiani, G. & Vogelstein, B. Only three driver gene mutations are required for the development of lung and colorectal cancers. Proc. Natl Acad. Sci. USA 112, 118–123 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Michaloglou, C. et al. BRAFE600-associated senescence-like cell cycle arrest of human naevi. Nature 436, 720–724 (2005). This paper reports the seminal observation that oncogenic mutations can exist in normal tissues without causing malignancies because of dynamically encoded safeguard mechanisms that cause OIS.

    Article  CAS  PubMed  Google Scholar 

  72. Courtois-Cox, S. et al. A negative feedback signaling network underlies oncogene-induced senescence. Cancer Cell 10, 459–472 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Yu, H. et al. The role of BRAF mutation and p53 inactivation during transformation of a subpopulation of primary human melanocytes. Am. J. Pathol. 174, 2367–2377 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Ha, L. et al. ARF functions as a melanoma tumor suppressor by inducing p53-independent senescence. Proc. Natl Acad. Sci. USA 104, 10968–10973 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Vredeveld, L. C. et al. Abrogation of BRAFV600E-induced senescence by PI3K pathway activation contributes to melanomagenesis. Genes Dev. 26, 1055–1069 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Cheung, M., Sharma, A., Madhunapantula, S. V. & Robertson, G. P. Akt3 and mutant V600EB-Raf cooperate to promote early melanoma development. Cancer Res. 68, 3429–3439 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Citri, A. & Yarden, Y. EGF–ERBB signalling: towards the systems level. Nat. Rev. Mol. Cell Biol. 7, 505–516 (2006).

    Article  CAS  PubMed  Google Scholar 

  78. Birtwistle, M. R. et al. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol. Syst. Biol. 3, 144 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Meyer, A. S., Miller, M. A., Gertler, F. B. & Lauffenburger, D. A. The receptor AXL diversifies EGFR signaling and limits the response to EGFR-targeted inhibitors in triple-negative breast cancer cells. Sci. Signal. 6, ra66 (2013).

    PubMed  PubMed Central  Google Scholar 

  80. Kholodenko, B. N., Hoek, J. B., Westerhoff, H. V. & Brown, G. C. Quantification of information transfer via cellular signal transduction pathways. FEBS Lett. 414, 430–434 (1997); erratum 419, 150 (1997).

    Article  CAS  PubMed  Google Scholar 

  81. 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. 9, 324–330 (2007). This paper shows that physiological ligands rewire network connections in order to produce their distinct biological outcomes.

    Article  CAS  PubMed  Google Scholar 

  82. von Kriegsheim, A. et al. Cell fate decisions are specified by the dynamic ERK interactome. Nat. Cell Biol. 11, 1458–1464 (2009).

    Article  CAS  PubMed  Google Scholar 

  83. Marshall, C. J. Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation. Cell 80, 179–185 (1995).

    Article  CAS  PubMed  Google Scholar 

  84. Niepel, M. et al. Analysis of growth factor signaling in genetically diverse breast cancer lines. BMC Biol. 12, 20 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Borisov, N. et al. Systems-level interactions between insulin–EGF networks amplify mitogenic signaling. Mol. Syst. Biol. 5, 256 (2009). This study elucidates the mechanistic basis and dynamic regulation of the crosstalk between the IR and EGFR pathways.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Wohrle, F. U., Daly, R. J. & Brummer, T. Function, regulation and pathological roles of the Gab/DOS docking proteins. Cell Commun. Signal 7, 22 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Kiyatkin, A. et al. Scaffolding protein Grb2-associated binder 1 sustains epidermal growth factor-induced mitogenic and survival signaling by multiple positive feedback loops. J. Biol. Chem. 281, 19925–19938 (2006).

    Article  CAS  PubMed  Google Scholar 

  88. Turke, A. B. et al. Preexistence and clonal selection of MET amplification in EGFR mutant NSCLC. Cancer Cell 17, 77–88 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Ortiz-Padilla, C. et al. Functional characterization of cancer-associated Gab1 mutations. Oncogene 32, 2696–2702 (2013).

    Article  CAS  PubMed  Google Scholar 

  90. Romano, D. et al. Protein interaction switches coordinate Raf-1 and MST2/Hippo signalling. Nat. Cell Biol. 16, 673–684 (2014). This paper describes the discovery of a new signalling motif and how competing protein interactions combined with changes in affinities can generate signalling switches that coordinate cell proliferation, survival and transformation.

    Article  CAS  PubMed  Google Scholar 

  91. Avruch, J. et al. Protein kinases of the Hippo pathway: regulation and substrates. Semin. Cell Dev. Biol. 23, 770–784 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Pan, D. The Hippo signaling pathway in development and cancer. Dev. Cell 19, 491–505 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Bao, Y., Hata, Y., Ikeda, M. & Withanage, K. Mammalian Hippo pathway: from development to cancer and beyond. J. Biochem. 149, 361–379 (2011).

    Article  CAS  PubMed  Google Scholar 

  94. Matallanas, D., Romano, D., Hamilton, G., Kolch, W. & O'Neill, E. A. Hippo in the ointment: MST signalling beyond the fly. Cell Cycle 7, 879–884 (2008).

    Article  CAS  PubMed  Google Scholar 

  95. O'Neill, E., Rushworth, L., Baccarini, M. & Kolch, W. Role of the kinase MST2 in suppression of apoptosis by the proto-oncogene product Raf-1. Science 306, 2267–2270 (2004).

    Article  CAS  PubMed  Google Scholar 

  96. Matallanas, D. et al. RASSF1A elicits apoptosis through an MST2 pathway directing proapoptotic transcription by the p73 tumor suppressor protein. Mol. Cell 27, 962–975 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Romano, D. et al. Proapoptotic kinase MST2 coordinates signaling crosstalk between RASSF1A, Raf-1, and Akt. Cancer Res. 70, 1195–1203 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Dhillon, A. S., Meikle, S., Yazici, Z., Eulitz, M. & Kolch, W. Regulation of Raf-1 activation and signalling by dephosphorylation. EMBO J. 21, 64–71 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Nguyen, L. K., Matallanas, D. G., Romano, D., Kholodenko, B. N. & Kolch, W. Competing to coordinate cell fate decisions: the MST2–Raf-1 signaling device. Cell Cycle 14, 189–199 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Matallanas, D. et al. Mutant K-Ras activation of the proapoptotic MST2 pathway is antagonized by wild-type K-Ras. Mol. Cell 44, 893–906 (2011).

    Article  CAS  PubMed  Google Scholar 

  101. Seidel, C. et al. Frequent hypermethylation of MST1 and MST2 in soft tissue sarcoma. Mol. Carcinog. 46, 865–871 (2007).

    Article  CAS  PubMed  Google Scholar 

  102. Grothey, A. & Lenz, H. J. Explaining the unexplainable: EGFR antibodies in colorectal cancer. J. Clin. Oncol. 30, 1735–1737 (2012).

    Article  CAS  PubMed  Google Scholar 

  103. To, M. D., Rosario, R. D., Westcott, P. M., Banta, K. L. & Balmain, A. Interactions between wildtype and mutant Ras genes in lung and skin carcinogenesis. Oncogene 32, 4028–4033 (2013).

    Article  CAS  PubMed  Google Scholar 

  104. Gabay, M., Li, Y. & Felsher, D. W. MYC activation is a hallmark of cancer initiation and maintenance. Cold Spring Harb. Perspect. Med. 4, a014241 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Wolf, E., Lin, C. Y., Eilers, M. & Levens, D. L. Taming of the beast: shaping Myc-dependent amplification. Trends Cell Biol. 25, 241–248 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Nie, Z. et al. c-Myc is a universal amplifier of expressed genes in lymphocytes and embryonic stem cells. Cell 151, 68–79 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Walz, S. et al. Activation and repression by oncogenic MYC shape tumour-specific gene expression profiles. Nature 511, 483–487 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Sabo, A. et al. Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature 511, 488–492 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Soucek, L. et al. Inhibition of Myc family proteins eradicates KRas-driven lung cancer in mice. Genes Dev. 27, 504–513 (2013). This study demonstrates that MYC function is irreplaceable in cancer and that, consequently, MYC is an important target for cancer therapy.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Dang, C. V. MYC on the path to cancer. Cell 149, 22–35 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Peter, S. et al. Tumor cell-specific inhibition of MYC function using small molecule inhibitors of the HUWE1 ubiquitin ligase. EMBO Mol. Med. 6, 1525–1541 (2014). This study shows how MYC function can be targeted with small-molecular-weight drugs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Muller, P. A. & Vousden, K. H. p53 mutations in cancer. Nat. Cell Biol. 15, 2–8 (2013).

    Article  CAS  PubMed  Google Scholar 

  114. Purvis, J. E. et al. p53 dynamics control cell fate. Science 336, 1440–1444 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Batchelor, E., Loewer, A., Mock, C. & Lahav, G. Stimulus-dependent dynamics of p53 in single cells. Mol. Syst. Biol. 7, 488 (2011). This paper describes the discovery and elucidation of the mechanisms underlying different p53 activation kinetics in response to different types of DNA damage.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Meek, D. W. & Anderson, C. W. Posttranslational modification of p53: cooperative integrators of function. Cold Spring Harb. Perspect. Biol. 1, a000950 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Hu, W. et al. A single nucleotide polymorphism in the MDM2 gene disrupts the oscillation of p53 and MDM2 levels in cells. Cancer Res. 67, 2757–2765 (2007).

    Article  CAS  PubMed  Google Scholar 

  118. Bond, G. L. et al. MDM2 SNP309 accelerates tumor formation in a gender-specific and hormone-dependent manner. Cancer Res. 66, 5104–5110 (2006).

    Article  CAS  PubMed  Google Scholar 

  119. O'Reilly, K. E. et al. mTOR inhibition induces upstream receptor tyrosine kinase signaling and activates Akt. Cancer Res. 66, 1500–1508 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Carracedo, A. et al. Inhibition of mTORC1 leads to MAPK pathway activation through a PI3K-dependent feedback loop in human cancer. J. Clin. Invest. 118, 3065–3074 (2008). This paper describes a feedback mechanism that limits the efficacy of a drug that held high promise for cancer treatment.

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012).

    Article  CAS  PubMed  Google Scholar 

  122. Klinger, B. et al. Network quantification of EGFR signaling unveils potential for targeted combination therapy. Mol. Syst. Biol. 9, 673 (2013). A systematic study using multiple perturbation experiments and mathematical modelling to identify vulnerable nodes in CRC cell lines and drug combinations that can address them.

    Article  PubMed  PubMed Central  Google Scholar 

  123. Sun, C. et al. Reversible and adaptive resistance to BRAF(V600E) inhibition in melanoma. Nature 508, 118–122 (2014). This study shows that resistance to RAF inhibitors is due to a network adaptation and elucidates the mechanism of this adaption.

    Article  CAS  PubMed  Google Scholar 

  124. Das Thakur, M. et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251–255 (2013).

    Article  CAS  PubMed  Google Scholar 

  125. Flaherty, K. T. et al. Combined BRAF and MEK inhibition in melanoma with BRAF V600 mutations. N. Engl. J. Med. 367, 1694–1703 (2012). The first study showing synergy between RAF and MEK inhibitors in a clinical setting.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Zhao, B., Pritchard, J. R., Lauffenburger, D. A. & Hemann, M. T. Addressing genetic tumor heterogeneity through computationally predictive combination therapy. Cancer Discov. 4, 166–174 (2014).

    Article  CAS  PubMed  Google Scholar 

  127. Zhao, B., Hemann, M. T. & Lauffenburger, D. A. Intratumor heterogeneity alters most effective drugs in designed combinations. Proc. Natl Acad. Sci. USA 111, 10773–10778 (2014). An intriguing paper suggesting new approaches to the design of drug combination therapies that can address tumour heterogeneity.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Lee, M. J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780–794 (2012). This paper presents a strong argument for designing drug combination therapies based on the hypothesis that one drug can push the network into a state that renders it highly vulnerable to the second drug.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Innominato, P. F. et al. The circadian timing system in clinical oncology. Ann. Med. 46, 191–207 (2014). A seminal study suggesting that mathematical modelling could be a powerful tool to stratify breast cancer patients.

    Article  CAS  PubMed  Google Scholar 

  130. Li, X. M. et al. A circadian clock transcription model for the personalization of cancer chronotherapy. Cancer Res. 73, 7176–7188 (2013).

    Article  CAS  PubMed  Google Scholar 

  131. Faratian, D. et al. Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab. Cancer Res. 69, 6713–6720 (2009).

    Article  CAS  PubMed  Google Scholar 

  132. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Bluthgen, N. et al. A systems biological approach suggests that transcriptional feedback regulation by dual-specificity phosphatase 6 shapes extracellular signal-related kinase activity in RAS-transformed fibroblasts. FEBS J. 276, 1024–1035 (2009).

    Article  PubMed  CAS  Google Scholar 

  134. Cornelius, S. P., Kath, W. L. & Motter, A. E. Realistic control of network dynamics. Nat. Commun. 4, 1942 (2013).

    Article  PubMed  CAS  Google Scholar 

  135. Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Vermeulen, L. et al. Defining stem cell dynamics in models of intestinal tumor initiation. Science 342, 995–998 (2013).

    Article  CAS  PubMed  Google Scholar 

  137. Song, J. H. et al. The APC network regulates the removal of mutated cells from colonic crypts. Cell Rep. 7, 94–103 (2014).

    Article  CAS  PubMed  Google Scholar 

  138. Byrne, H. M. Dissecting cancer through mathematics: from the cell to the animal model. Nat. Rev. Cancer 10, 221–230 (2010).

    Article  CAS  PubMed  Google Scholar 

  139. Goldbeter, A. & Koshland, D. E. Jr. An amplified sensitivity arising from covalent modification in biological systems. Proc. Natl Acad. Sci. USA 78, 6840–6844 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Murphy, L. O., Smith, S., Chen, R. H., Fingar, D. C. & Blenis, J. Molecular interpretation of ERK signal duration by immediate early gene products. Nat. Cell Biol. 4, 556–564 (2002).

    Article  CAS  PubMed  Google Scholar 

  141. Nakakuki, T. et al. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Cell 141, 884–896 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Amit, I. et al. A module of negative feedback regulators defines growth factor signaling. Nat. Genet. 39, 503–512 (2007).

    Article  CAS  PubMed  Google Scholar 

  143. Goentoro, L. & Kirschner, M. W. Evidence that fold-change, and not absolute level, of β-catenin dictates Wnt signaling. Mol. Cell 36, 872–884 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Ma, W., Trusina, A., El-Samad, H., Lim, W. A. & Tang, C. Defining network topologies that can achieve biochemical adaptation. Cell 138, 760–773 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Volinsky, N. & Kholodenko, B. N. Complexity of receptor tyrosine kinase signal processing. Cold Spring Harb. Perspect. Biol. 5, a009043 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

This work was supported by the Science Foundation Ireland under grant number 06/CE/B1129 and the European Union FP7 (Seventh Framework Programme for Research and Technological Development) under grant number 259348–2 ASSET (Analysing and Striking the Sensitivities of Embryonal Tumours).

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Glossary

Cybernetics

A field of science that studies the properties and topology of systems that receive, store and process information to use it for control purposes.

Network topology

The structure of a network as determined by the nodes (components) and edges (connections between them).

Bistability (or multistability)

Refers to a system having two (or more) stable states. For instance, a switch is a bistable system that can be in either an ON or an OFF state.

Feedback loops

Network motifs in which a downstream molecule (B) affects the activity of its upstream regulator (A). Feedback loops can be negative (where B inhibits A) or positive (where B activates A).

'AND' gate

A function that requires two simultaneous inputs to elicit an output. By contrast, an 'OR' gate requires only one of two possible inputs to produce an output.

Amplifier

A device or circuit that increases the amplitude of an input signal. For example, kinase cascades, in which a kinase phosphorylates and activates another kinase, can act as amplifiers if the downstream kinase is more abundant than the upstream kinase. Amplifiers can generate linear or nonlinear gains in signal strengths.

Negative-feedback amplifier

(NFA). A circuit in which the output of an amplifier is linked back to the input through a negative-feedback loop. In this constellation, the strength of the negative feedback determines the dynamic behaviour of the circuit. The NFA structure is common to the ubiquitous MAPK pathways, in which a three-tiered kinase cascade functions as an amplifier and the terminal MAPK feedback inhibits the first kinase in the cascade.

Noise-buffering signal integrator

A network circuit in which two components are linked by mutually negative feedbacks that only allow monostable states: that is, when one component is ON the other is OFF. This circuit can integrate signals while efficiently buffering noise, as the output is a digital ON or OFF state.

Driver mutation

A mutation that is necessary for, and drives the pathogenesis of, a disease. By contrast, a passenger mutation is a mutation that occurs coincidentally and is dispensable for pathogenesis.

Feedforward loops

Network motifs in which an upstream regulator (A) affects the activity of a downstream node (C) directly as well as indirectly via an intermediate node (B). If both pathways connecting A to C activate C, the feedforward loop is called coherent. If one pathway is inhibitory and the other stimulatory, the feedforward loop is termed incoherent.

Coincidence detector

A device that detects whether two or more signals coincide in time and/or space. For example, an 'AND' gate can function as a coincidence detector.

Pulsatile

Indicates a pulsating or rhythmic change in the amplitude of a signal input or output response.

Toggle switch

An ON or OFF switch that is operated by 'flipping' a lever. In network biology, this term is used to indicate configurations in which a certain input condition, like a lever, switches an output state ON or OFF.

Chronotherapy

Refers to the timing of the application of a therapy to maximize its desired effects and/or minimize its side effects.

Multiscale modelling

Modelling of systems across different scales, such as atomistic, molecular, subcellular and cellular. Sometimes used to indicate that different modelling methods (for example, deterministic and stochastic methods) are combined in order to describe a system.

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Kolch, W., Halasz, M., Granovskaya, M. et al. The dynamic control of signal transduction networks in cancer cells. Nat Rev Cancer 15, 515–527 (2015). https://doi.org/10.1038/nrc3983

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