Advertisement

Network-Oriented Approaches to Anticancer Drug Response

  • Paola Lecca
  • Angela ReEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1513)

Abstract

A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the “one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations. The complexity of the molecular and cellular bases of systems responses to drug interventions has fostered an increased interest in systems-oriented approaches to drug discovery. Consonant with this knowledge of the multifactorial mechanistic basis of drug sensitivity and resistance is the application of network-based approaches for the identification of molecular (multi-)feature signatures associated with desired (multi-)drug phenotypic profiles. This chapter illustrates the principal network analysis and inference techniques which have found application in systems-oriented drug design and considers their benefits and drawbacks in relation to the nature of the data produced by network pharmacology.

Key words

Drug–target networks Drug discovery Cancer systems biology Biological network inference Network vulnerability analysis 

References

  1. 1.
    Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664CrossRefPubMedGoogle Scholar
  2. 2.
    Barabási AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113CrossRefPubMedGoogle Scholar
  3. 3.
    Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms, 2nd edn. MIT Press and McGraw-Hill Book Company, Cambridge, MAGoogle Scholar
  4. 4.
    Kitano H (2001) Exploring complex networks. Nature 410:268–276CrossRefGoogle Scholar
  5. 5.
    Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435:814–818CrossRefPubMedGoogle Scholar
  6. 6.
    Al-Lazikani B, Banerji U, Workman P (2012) Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol 30:679–692CrossRefPubMedGoogle Scholar
  7. 7.
    Keith CT, Borisy AA, Stockwell BR (2005) Multicomponent therapeutics for networked systems. Nat Rev Drug Discov 4:71–78CrossRefPubMedGoogle Scholar
  8. 8.
    Foucquier J, Guedj M (2015) Analysis of drug combinations: current methodological landscape. Pharmacol Res Perspect 3:e00149CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Kitano H (2004) Biological robustness. Nat Rev Genet 5:826–837CrossRefPubMedGoogle Scholar
  10. 10.
    Stelling J, Sauer U, Szallasi Z et al (2004) Robustness of cellular functions. Cell 118:675–685CrossRefPubMedGoogle Scholar
  11. 11.
    Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461CrossRefPubMedGoogle Scholar
  12. 12.
    Vidal M, Cusick ME, Barabási AL (2011) Interactome networks and human disease. Cell 144:986–998CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Covell DG (2015) Data mining approaches for genomic biomarker development: applications using drug screening data from the cancer genome project and the cancer cell line encyclopedia. PLoS One 10, e0127433CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Lamb J, Crawford ED, Peck D et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935CrossRefPubMedGoogle Scholar
  15. 15.
    Woo JH, Shimoni Y, Yang WS et al (2015) Elucidating compound mechanism of action by network perturbation analysis. Cell 162:441–451CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zhu M, Gao L, Li X et al (2009) The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network. J Drug Target 17:524–532CrossRefPubMedGoogle Scholar
  17. 17.
    Jeong H, Mason SP, Barabśi AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411:41–42CrossRefPubMedGoogle Scholar
  18. 18.
    Khuri S, Wuchty S (2012) Essentiality and centrality in protein interaction networks revisited. BMC Bioinformatics 16:109CrossRefGoogle Scholar
  19. 19.
    Brush ER, Krakauer DC, Flack JC (2013) A family of algorithms for computing consensus about node state from network data. PLoS Comput Biol 9:e1003109CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Yang L, Wang J, Wang H et al (2014) Characterization of essential genes by topological properties in the perturbation sensitivity network. Biochem Biophys Res Commun 448:473–479CrossRefPubMedGoogle Scholar
  21. 21.
    Wang X, Thijssen B, Yu H (2013) Target essentiality and centrality characterize drug side effects. PLoS Comput Biol 9:e1003119CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Peng Q, Schork NJ (2014) Utility of network integrity methods in therapeutic target identification. Front Genet 5:12CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Barzel B, Barabási AL (2013) Universality in network dynamics. Nat Phys 9Google Scholar
  24. 24.
    Estrada E (2012) The structure of complex networks. Theory and applications, 1st edn. Oxford University Press, Oxford, UKGoogle Scholar
  25. 25.
    Sams-Dodd F (2005) Target-based drug discovery: is something wrong? Drug Discov Today 10:139–147CrossRefPubMedGoogle Scholar
  26. 26.
    Kitano H (2010) Violations of robustness trade-offs. Mol Syst Biol 6Google Scholar
  27. 27.
    Kassouf W, Dinney CP, Brown G et al (2005) Uncoupling between epidermal growth factor receptor and downstream signals defines resistance to the antiproliferative effect of gefitinib in bladder cancer cells. Cancer Res 65(10):524–535Google Scholar
  28. 28.
    Sergina NV, Rausch M, Wang D et al (2007) Escape from her-family tyrosine kinase inhibitor therapy by the kinase-inactive her3. Nature 445:437–441CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Meng J, Peng H, Dai B et al (2009) High level of akt activity is associated with resistance to mek inhibitor azd6244 (arry-142886). Cancer Biol Ther 8:2073–2080CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Rodrik-Outmezguine VS, Chandarlapaty S, Pagano NC et al (2011) mTOR kinase inhibition causes feedback-dependent biphasic regulation of akt signaling. Cancer Discov 1:248–259CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    FitzGerald GA, Patrono C (2001) The coxibs, selective inhibitors of cyclooxygenase-2. N Engl J Med 345:433–442CrossRefPubMedGoogle Scholar
  32. 32.
    Ito T, Ando H, Suzuki T et al (2010) Identification of a primary target of thalidomide teratogenicity. Science 327:1345–1350CrossRefPubMedGoogle Scholar
  33. 33.
    Jia J, Zhu F, Ma X et al (2009) Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov 8:111–128CrossRefPubMedGoogle Scholar
  34. 34.
    Modi S, Stopeck A, Linden H et al (2011) Hsp90 inhibition is effective in breast cancer: a phase ii trial of tanespimycin (17-aag) plus trastuzumab in patients with her2-positive metastatic breast cancer progressing on trastuzumab. Clin Cancer Res 17:5132–5139CrossRefPubMedGoogle Scholar
  35. 35.
    Nahta R, Hung MC, Esteva FJ (2004) The her-2-targeting antibodies trastuzumab and pertuzumab synergistically inhibit the survival of breast cancer cells. Cancer Res 64:2343–2346CrossRefPubMedGoogle Scholar
  36. 36.
    Meng J, Dai B, Fang B et al (2010) Combination treatment with mek and akt inhibitors is more effective than each drug alone in human non-small cell lung cancer in vitro and in vivo. Cell 5:e14124Google Scholar
  37. 37.
    Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690CrossRefPubMedGoogle Scholar
  38. 38.
    Pe’er D, Hacohen N (2011) Principles and strategies for developing network models in cancer. Cell 144:864–873CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Zotenko E, Mestre J, O’Leary DP et al (2008) Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality. PLoS Comput Biol 4:e1000140CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Yu H, Kim PM, Sprecher E et al (2007) The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3:e59CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Hwang WC, Zhang A, Ramanathan M (2008) Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery. Clin Pharmacol Ther 84:563–572CrossRefPubMedGoogle Scholar
  42. 42.
    Nacher JC, Schwartz JM (2008) A global view of drug-therapy interactions. BMC Pharmacol 8Google Scholar
  43. 43.
    Almaas E, Kovács B, Vicsek T et al (2004) Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427:839–843CrossRefPubMedGoogle Scholar
  44. 44.
    Csermely P (2004) Strong links are important, but weak links stabilize them. Trends Biochem Sci 29:331–334CrossRefPubMedGoogle Scholar
  45. 45.
    Zhang X, Zhang Z, Zhao H et al (2014) Extracting the globally and locally adaptive backbone of complex networks. PLoS One 9:e100428CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Estrada E, Hatano N (2010) A vibrational approach to node centrality and vulnerability in complex networks. Physica A 389:3648–3660CrossRefGoogle Scholar
  47. 47.
    Birtwistle MR, Hatakeyama M, Yumoto N et al (2007) Ligand-dependent responses of the erbb signaling network: experimental and modeling analyses. Mol Syst Biol 3:144CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Iadevaia S, Lu Y, Morales FC et al (2010) Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res 70:6704–6714CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Chou IC, Voit EO (2009) Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math Biosci 219:57–83CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Liu Y, Gunawan R (2014) Parameter estimation of dynamic biological network models using integrated fluxes. BMC Syst Biol 8:127CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Faratian D, Goltsov A, Lebedeva G et al (2009) Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of pten in resistance to trastuzumab. Cancer Res 69:6713–6720CrossRefPubMedGoogle Scholar
  52. 52.
    Morris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA (2010) Logic-based models for the analysis of cell signaling networks. Biochemistry 49:3216–3224CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Lee MJ, Ye AS, Gardino AK et al (2012) Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149:780–794CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Sahin O, Fröhlich H, Löbke C et al (2009) Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance. BMC Syst Biol 3:1CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Aldridge BB, Saez-Rodriguez J, Muhlich JL et al (2009) Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling. PLoS Comput Biol 5:e1000340CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Sachs K, Perez O, Pe’er D et al (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308:523–529CrossRefPubMedGoogle Scholar
  57. 57.
    Barretina J, Caponigro G, Stransky N et al (2012) The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Shoemaker RH (2006) The nci60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6:813–823CrossRefPubMedGoogle Scholar
  59. 59.
    Pal R, Berlow N (2012) A kinase inhibition map approach for tumor sensitivity prediction and combination therapy design for targeted drugs. Pac Symp Biocomput. pp 351–362Google Scholar
  60. 60.
    Tang J, Karhinen L, Xu T et al (2013) Target inhibition networks: Predicting selective combinations of druggable targets to block cancer survival pathways. PLoS Comput Biol 9:e1003226CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Ranjan G, Zhang ZL (2013) Geometry of complex networks and topological centrality. Physica A 392:3833–3845CrossRefGoogle Scholar
  62. 62.
    Estrada E, Hatano H (2010) Resistance distance, information centrality, node vulnerability and vibrations in complex networks. In: Estrada E, Fox M, Higham D, Oppo GL (eds) Network science: complexity in nature and technology. Springer, New York, pp 13–29CrossRefGoogle Scholar
  63. 63.
    Yang W, Soares J, Greninger P et al (2013) Genomics of drug sensitivity in cancer (gdsc): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41(Database issue):D955–61CrossRefPubMedGoogle Scholar
  64. 64.
    Cappuccio A, Zollinger R, Schenk M et al (2015) Combinatorial code governing cellular responses to complex stimuli. Nat Commun 6:e0127433CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of TrentoPovo TrentoItaly
  2. 2.Senior Member of Association for Computing MachineryNew YorkUSA
  3. 3.Laboratory of Computational Oncology, Centre for Integrative BiologyUniversity of TrentoTrentoItaly

Personalised recommendations