Advertisement

pp 1-38 | Cite as

Predicting Tumor Growth and Ligand Dependence from mRNA by Combining Machine Learning with Mechanistic Modeling

  • Helge Hass
  • Andreas RaueEmail author
Protocol
Part of the Methods in Pharmacology and Toxicology book series

Abstract

For successful treatment of cancer patients, it is crucial to identify subgroups that respond to certain types of targeted therapy. A key element for tumor growth is the abundance of receptors and binding of growth factors, which can be diminished via therapeutic antibodies. Here, a mechanistic signaling network model is linked to patient-specific ribonucleic acid sequencing data (RNAseq), enabling the prediction of individuals susceptible to a particular medication. The mechanistic model comprises multiple receptors and their dimerization, and is calibrated using time-resolved in-vitro data. Further, the model is combined with in-vitro cell viability measurements via a machine learning algorithm and ultimately applied to patient-derived data to predict ligand dependence of tumors. For this purpose, RNA sequencing data are exploited to constrain model parameters and generalize model response. Mathematical modeling of signal transduction is used as a mediator, performing a non-trivial transformation of initial protein expression levels and ligand conditions to cell-type specific response. Thereby, it allows for bridging the gap between studies of signal transduction on a short time scale and cell fate decisions in the long term, potentially aiding in drug development, patient stratification, and prediction of tumor response. This chapter is based on work previously published in Hass et al. (NPJ Syst Biol Appl 3(1):27, 2017) and Hass (Quantifying cell biology: mechanistic dynamic modeling of receptor crosstalk. PhD thesis, Albert-Ludwigs-Universität Freiburg, 2017).

Keywords

Cancer cell lines Computational biology Decision trees Growth response Machine learning Mechanistic modeling Prediction of responses Receptor tyrosine kinase Signaling pathways 

Notes

Acknowledgements

We thank Tim Heinemann, Jeffrey Kearns, Sergio Iadevaia, Yasmin Hasham-bhoy-Ramsay, and Tim Maiwald for their constructive feedback and proof reading the manuscript.

References

  1. 1.
    Adlung L, Kar S, Wagner MC, She B, Chakraborty S, Bao J, Lattermann S, Boerries M, Busch H, Wuchter P, Ho AD, Timmer J, Schilling M, Höfer T, Klingmüller U (2017) Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation. Mol Syst Biol 13(1):904Google Scholar
  2. 2.
    Altman RB (2015) Predicting cancer drug response: advancing the DREAM. Cancer Discov 5(3):237–238Google Scholar
  3. 3.
    Arteaga CL (2002) Epidermal growth factor receptor dependence in human tumors: more than just expression? Oncologist 7(suppl 4):31–39Google Scholar
  4. 4.
    Arteaga CL, Engelman JA (2014) ErbB receptors: from oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell 25(3):282–303Google Scholar
  5. 5.
    ATLAS Collaboration (2015) Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector. J High Energy Phys 2015(4):117Google Scholar
  6. 6.
    Bansal M, Yang J, Karan C, Menden MP, Costello JC, Tang H, Xiao G, Li Y, Allen J, Zhong R, Chen B, Kim M, Wang T, Heiser LM, Realubit R, Mattioli M, Alvarez MJ, Shen Y, Community ND, Gallahan D, Singer D, Saez-Rodriguez J, Xie Y, Stolovitzky G, Califano A (2014) A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol 32(12):1213–1222Google Scholar
  7. 7.
    Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jané-Valbuena J, Mapa FA, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels IH, Cheng J, Yu GK, Yu J, Aspesi P, de Silva M, Jagtap K, Jones MD, Wang L, Hatton C, Palescandolo E, Gupta S, Mahan S, Sougnez C, Onofrio RC, Liefeld T, MacConaill L, Winckler W, Reich M, Li N, Mesirov JP, Gabriel SB, Getz G, Ardlie K, Chan V, Myer VE, Weber BL, Porter J, Warmuth M, Finan P, Harris JL, Meyerson M, Golub TR, Morrissey MP, Sellers WR, Schlegel R, Garraway LA (2012) The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603–607Google Scholar
  8. 8.
    Basilico C, Arnesano A, Galluzzo M, Comoglio PM, Michieli P (2008) A high affinity hepatocyte growth factor-binding site in the immunoglobulin-like region of MET. J Biol Chem 283(30):21267–21277Google Scholar
  9. 9.
    Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees, vol 19. CRC, Boca RatonGoogle Scholar
  10. 10.
    Chong CR, Jänne Pa (2013) The quest to overcome resistance to EGFR-targeted therapies in cancer. Nat Med 19(11):1389–1400Google Scholar
  11. 11.
    Citri A, Yarden Y (2006) EGF-ErbB signalling: towards the systems level. Nat Rev Mol Cell Biol 7(7):505–516Google Scholar
  12. 12.
    Coleman T, Li Y (1996) An interior, trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 6:418–445Google Scholar
  13. 13.
    Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, Bansal M, Ammad-Ud-Din M, Hintsanen P, Khan Sa, Mpindi JP, Kallioniemi O, Honkela A, Aittokallio T, Wennerberg K, Collins JJ, Gallahan D, Singer D, Saez-Rodriguez J, Kaski S, Gray JW, Stolovitzky G (2014) A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 32(12):1–103Google Scholar
  14. 14.
    Endo H, Okuyama H, Ohue M, Inoue M (2014) Dormancy of cancer cells with suppression of AKT activity contributes to survival in chronic hypoxia. PLoS ONE 9(6):e98858Google Scholar
  15. 15.
    Engelman Ja, Zejnullahu K, Mitsudomi T, Song Y, Hyland C, Park JO, Lindeman N, Gale CM, Zhao X, Christensen J, Kosaka T, Holmes AJ, Rogers AM, Cappuzzo F, Mok T, Lee C, Johnson BE, Cantley LC, Jänne Pa (2007) MET amplification leads to gefitinib resistance in lung cancer by activating ErbB3 signaling. Science 316(5827):1039–1043Google Scholar
  16. 16.
    FDA (2015) Accelerating the development of new pharmaceutical therapies. Technical report, US Food and Drug AdministrationGoogle Scholar
  17. 17.
    Fisher RA (1912) On an absolute criterion for fitting frequency curves. Messenger Math 41:155–160Google Scholar
  18. 18.
    Gazdar AF, Shigematsu H, Herz J, Minna JD (2004) Mutations and addiction to EGFR: The Achilles ‘heal’ of lung cancers? Trends Mol Med 10(10):481–486Google Scholar
  19. 19.
    Hannah R, Beck M, Moravec R, Riss T (2001) CellTiter-Glo™ Luminescent cell viability assay: a sensitive and rapid method for determining cell viability. Promega Cell Notes 2:11–13Google Scholar
  20. 20.
    Hass H (2017) Quantifying cell biology: Mechanistic dynamic modeling of receptor crosstalk. PhD thesis, Albert-Ludwigs-Universität FreiburgGoogle Scholar
  21. 21.
    Hass H, Masson K, Wohlgemuth S, Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J, MacBeath G, Schoeberl B, Raue A (2017) Predicting ligand-dependent tumors from multi-dimensional signaling features. NPJ Syst Biol Appl 3(1):27Google Scholar
  22. 22.
    Heinrich R, Neel BG, Rapoport TA (2002) Mathematical models of protein kinase signal transduction. Mol Cell 9(5):957–970Google Scholar
  23. 23.
    Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK, Graim K, Bivol A, Wang H, Zhu F, Afsari B, Danilova LV, Favorov AV, Lee WS, Taylor D, Hu CW, Long BL, Noren DP, Bisberg AJ, HPN-DREAM Consortium, Mills GB, Gray JW, Kellen M, Norman T, Friend S, Qutub AA, Fertig EJ, Guan Y, Song M, Stuart JM, Spellman PT, Koeppl H, Stolovitzky G, Saez-Rodriguez J, Mukherjee S (2016) Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods 13(4):310–318Google Scholar
  24. 24.
    Hindmarsh AC, Brown PN, Grant KE, Lee SL, Serban R, Shumaker DE, Woodward CS (2005) SUNDIALS: suite of nonlinear and differential/algebraic equation solvers. ACM Trans Math Softw 31(3):363–396Google Scholar
  25. 25.
    Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG (2013) Cancer drug resistance: an evolving paradigm. Nat Rev Cancer 13(10):714–726Google Scholar
  26. 26.
    Howlader N, Noone A, Krapcho M, Miller D, Bishop K, Altekruse S, Kosary C, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis D, Chen H, Feuer E, Cronin Ke (2016) SEER cancer statistics review, 1975–2013. Technical report, National Cancer Institute, BethesdaGoogle Scholar
  27. 27.
    Jin Q, Esteva FJ (2008) Cross-talk between the ErbB/HER family and the type I insulin-like growth factor receptor signaling pathway in breast cancer. J Mammary Gland Biol Neoplasia 13(4):485–498Google Scholar
  28. 28.
    Kaushansky A, Allen JE, Gordus A, Stiffler MA, Karp ES, Chang BH, MacBeath G (2010) Quantifying protein-protein interactions in high throughput using protein domain microarrays. Nat Protoc 5(4):773–790Google Scholar
  29. 29.
    Kearns JD, Bukhalid R, Sevecka M, Tan G, Gerami-Moayed N, Werner SL, Kohli N, Burenkova O, Sloss CM, King AM, Fitzgerald JB, Nielsen UB, Wolf BB (2015) Enhanced targeting of the EGFR network with MM-151, an oligoclonal anti-EGFR antibody therapeutic. Mol Cancer Ther 14(7):1625–1636Google Scholar
  30. 30.
    Kholodenko B (2006) Cell-signalling dynamics in time and space. Natl Rev Mol Cell Biology 7(3):165–176Google Scholar
  31. 31.
    Kholodenko BN, Demin OV, Moehren G, Hoek JB (1999) Quantification of short term signaling by the epidermal growth factor receptor. J Biol Chem 274(42):30169–30181Google Scholar
  32. 32.
    Kitano H (2002) Systems biology: a brief overview. Science 295(5560):1662–1664Google Scholar
  33. 33.
    Knight-Schrijver V, Chelliah V, Cucurull-Sanchez L, Le Novère N (2016) The promises of quantitative systems pharmacology modelling for drug development. Comput Struct Biotechnol J 14:363–370Google Scholar
  34. 34.
    Kreutz C, Rodriguez MMB, Maiwald T, Seidl M, Blum HE, Mohr L, Timmer J (2007) An error model for protein quantification. Bioinformatics 23(20):2747–2753Google Scholar
  35. 35.
    Kris M, Natale R, Herbst R, Lynch TJ, Prager D, Belani J, Schiller J, Kelly K, Spiridonidis H, Sandler A, Albain K (2003) Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non–small cell lung cancer: a randomized trial. J Am Med Assoc 290(16):2149–2158Google Scholar
  36. 36.
    Lai AZ, Abella JV, Park M (2009) Crosstalk in MET receptor oncogenesis. Trends Cell Biology 19(10):542–551Google Scholar
  37. 37.
    Laplante M, Sabatini DM (2012) mTOR signaling in growth control and disease. Cell 149(2):274–293Google Scholar
  38. 38.
    Laurent-Puig P, Cayre A, Manceau G, Buc E, Bachet JB, Lecomte T, Rougier P, Lievre A, Landi B, Boige V, Ducreux M, Ychou M, Bibeau F, Bouché O, Reid J, Stone S, Penault-Llorca F (2009) Analysis of PTEN, BRAF, and EGFR status in determining benefit from cetuximab therapy in wild-type KRAS metastatic colon cancer. J Clin Oncol 27(35):5924–5930Google Scholar
  39. 39.
    Ledford H (2011) Ways to fix the clinical trial. Nature 477:526–528Google Scholar
  40. 40.
    Liu F, Wang L, Perna F, Nimer SD (2016) Beyond transcription factors: how oncogenic signalling reshapes the epigenetic landscape. Nat Rev Cancer 16(6):359–372Google Scholar
  41. 41.
    Luey BC, May FEB (2016) Insulin-like growth factors are essential to prevent anoikis in oestrogen-responsive breast cancer cells: importance of the type I IGF receptor and PI3-kinase/Akt pathway. Mol Cancer 15(1):8Google Scholar
  42. 42.
    Luo J, Solimini NL, Elledge SJ (2009) Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136(5):823–837Google Scholar
  43. 43.
    Lyashenko E, Niepel M, Dixit P, Lim SK, Sorger P, Vitkup D (2017) Receptor-based mechanism of relative sensing in mammalian signaling networks. bioRxiv https://doi.org/10.1101/158774
  44. 44.
    Macdonald-Obermann JL, Pike LJ (2014) Different epidermal growth factor (EGF) receptor ligands show distinct kinetics and biased or partial agonism for homodimer and heterodimer formation. J Biol Chem 289(38):26178–26188Google Scholar
  45. 45.
    Magnuson B, Ekim B, Fingar DC (2012) Regulation and function of ribosomal protein S6 kinase (S6K) within mTOR signalling networks. Biochem J 441(1):1–21Google Scholar
  46. 46.
    Maher KJ, Fletcher MA (2005) Quantitative flow cytometry in the clinical laboratory. Clin Appl Immunol Rev 5(6):353–372Google Scholar
  47. 47.
    Maiwald T, Hass H, Steiert B, Vanlier J, Engesser R, Raue A, Kipkeew F, Bock HH, Kaschek D, Kreutz C, Timmer J (2016) Driving the model to its limit: profile likelihood based model reduction. PLoS ONE 11(9):e0162366Google Scholar
  48. 48.
    Marshall C (1995) Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation. Cell 80(2):179–185Google Scholar
  49. 49.
    Masuda H, Zhang D (2012) Role of epidermal growth factor receptor in breast cancer. Breast Cancer Res Treat 136(2):1–21Google Scholar
  50. 50.
    Menden MP, Iorio F, Garnett M, McDermott U, Benes CH, Ballester PJ, Saez-Rodriguez J (2013) Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS ONE 8(4):e61318Google Scholar
  51. 51.
    Mendoza MC, Er EE, Blenis J (2011) The RAS-ERK and PI3K-mTOR pathways: cross-talk and compensation. Trends Biochem Sci 36(6):320–328Google Scholar
  52. 52.
    Moroni M, Veronese S, Benvenuti S, Marrapese G, Sartore-Bianchi A, Di Nicolantonio F, Gambacorta M, Siena S, Bardelli A (2005) Gene copy number for epidermal growth factor receptor (EGFR) and clinical response to anti–EGFR treatment in colorectal cancer: a cohort study. Lancet Oncology 6(5):279–286Google Scholar
  53. 53.
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628Google Scholar
  54. 54.
    Mullard A (2015) 2014 FDA drug approvals. Nature 14(2):77–81Google Scholar
  55. 55.
    Nelson MR, Johnson T, Warren L, Hughes AR, Chissoe SL, Xu CF, Waterworth DM (2016) The genetics of drug efficacy: opportunities and challenges. Nat Rev Genet 17(4):197–206Google Scholar
  56. 56.
    Niepel M, Hafner M, Pace EA, Chung M, Chai DH, Zhou L, Schoeberl B, Sorger PK (2013) Profiles of Basal and stimulated receptor signaling networks predict drug response in breast cancer lines. Sci Signal 6(294):ra84Google Scholar
  57. 57.
    Oda K, Matsuoka Y, Funahashi A, Kitano H (2005) A comprehensive pathway map of epidermal growth factor receptor signaling. Mol Syst Biol 1:2005.0010Google Scholar
  58. 58.
    Organ SL, Tsao MS (2011) An overview of the c-MET signaling pathway. Ther Adv Medl Oncol 3(1):S7–S19Google Scholar
  59. 59.
    Paez JG, Jänne PA, Lee JC, Tracy S, Greulich H, Gabriel S, Herman P, Kaye FJ, Lindeman N, Boggon TJ, Naoki K, Sasaki H, Fujii Y, Eck MJ, Sellers WR, Johnson BE, Meyerson M (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304(5676):1497–500Google Scholar
  60. 60.
    Peterson M, Riggs M (2015) FDA advisory meeting clinical pharmacology review utilizes a quantitative systems pharmacology (QSP) model: a watershed moment? Pharmacometrics Syst Pharmacol 4(3):189–192Google Scholar
  61. 61.
    Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, Graim K, Funk C, Verspoor K, Ben-Hur A, Pandey G, Yunes JM (2013) A large-scale evaluation of computational protein function prediction. Nat Methods 10(3):221–227Google Scholar
  62. 62.
    Raman DV, Anderson J, Papachristodoulou A (2017) Delineating parameter unidentifiabilities in complex models. Phys Rev E 95(3):032314Google Scholar
  63. 63.
    Raue A, Schilling M, Bachmann J, Matteson A, Schelker M, Kaschek D, Hug S, Kreutz C, Harms BD, Theis FJ et al (2013) Lessons learned from quantitative dynamical modeling in systems biology. PLoS ONE 8(9):e74335Google Scholar
  64. 64.
    Raue A, Steiert B, Schelker M, Kreutz C, Maiwald T, Hass H, Vanlier J, Tönsing C, Adlung L, Engesser R et al (2015) Data2dynamics: a modeling environment tailored to parameter estimation in dynamical systems. Bioinformatics 31(21):3558–3560Google Scholar
  65. 65.
    Ren Y, Cao B, Law S, Xie Y, Lee PY, Cheung L, Chen Y, Huang X, Chan HM, Zhao P, Luk J, Vande Woude G, Wong J (2005) Hepatocyte growth factor promotes cancer cell migration and angiogenic factors expression: a prognostic marker of human esophageal squamous cell carcinomas. Clin Cancer Res 11(17):6190–6197Google Scholar
  66. 66.
    Rosenblatt M, Timmer J, Kaschek D (2016) Customized steady-state constraints for parameter estimation in non-linear ordinary differential equation models. Front Cell Dev Biol 4(41)Google Scholar
  67. 67.
    Ryerson AB, Eheman CR, Altekruse SF, Ward JW, Jemal A, Sherman RL, Henley SJ, Holtzman D, Lake A, Noone AM, Anderson RN, Ma J, Ly KN, Cronin KA, Penberthy L, Kohler BA (2016) Annual report to the nation on the status of cancer, 1975–2012, featuring the increasing incidence of liver cancer. Cancer 122(9):1312–1337Google Scholar
  68. 68.
    Schapire RE (1990) The strength of weak learnability. Mach. Learn. 5(2):197–227Google Scholar
  69. 69.
    Schoeberl B, Pace E, Howard S, Garantcharova V, Kudla A, Sorger PK, Nielsen UB (2006) A data-driven computational model of the ErbB receptor signaling network. In: Annual international conference of the IEEE engineering in medicine and biology - proceedings, pp 53–54Google Scholar
  70. 70.
    Schoeberl B, Pace Ea, Fitzgerald JB, Harms BD, Xu L, Nie L, Linggi B, Kalra AV, Paragas V, Bukhalid R, Grantcharova V, Kohli N, West Ka, Leszczyniecka M, Feldhaus MJ, Kudla AJ, Nielsen UB (2009) Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2(77):ra31Google Scholar
  71. 71.
    Schoeberl B, Kudla A, Masson K, Kalra A, Curley M, Finn G, Pace E, Harms B, Kim J, Kearns J, Fulgham A, Burenkova O, Grantcharova V, Yarar D, Paragas V, Fitzgerald J, Wainszelbaum M, West K, Mathews S, Nering R, Adiwijaya B, Garcia G, Kubasek B, Moyo V, Czibere A, Nielsen UB, MacBeath G (2017) Systems biology driving drug development: from design to the clinical testing of the anti-ErbB3 antibody seribantumab (MM-121). NPJ Syst Biol Appl 3:16034Google Scholar
  72. 72.
    Schumacher R, Mosthaf L, Schlessinger J, Brandenburg D, Ullrich A (1991) Insulin and insulin-like growth factor-1 binding specificity is determined by distinct regions of their cognate receptors. J Biol Chem 266(29):19288–19295Google Scholar
  73. 73.
    Sevecka M, MacBeath G (2006) State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling. Nat Methods 3(10):825–831Google Scholar
  74. 74.
    Sevecka M, Wolf-Yadlin A, MacBeath G (2011) Lysate microarrays enable high-throughput, quantitative investigations of cellular signaling. Mol Cell Proteomics 10(4):M110.005363Google Scholar
  75. 75.
    Sharma SV, Bell DW, Settleman J, Haber DA (2007) Epidermal growth factor receptor mutations in lung cancer. Nat Rev Cancer 7(3):169–181Google Scholar
  76. 76.
    Shi T, Niepel M, McDermott JE, Gao Y, Nicora CD, Chrisler WB, Markillie LM, Petyuk VA, Smith RD, Rodland KD et al (2016) Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway. Sci Signal 9(436):rs6Google Scholar
  77. 77.
    Sigismund S, Woelk T, Puri C, Maspero E, Tacchetti C, Transidico P, Di Fiore PP, Polo S (2005) Clathrin-independent endocytosis of ubiquitinated cargos. Proc Natl Acad Sci USA 102(8):2760–2765Google Scholar
  78. 78.
    Sliwkowski MX, Schaefer G, Akita RW, Lofgren JA, Fitzpatrick VD, Nuijens A, Fendly BM, Cerione RA, Vandlen RL, Carraway KL (1994) Coexpression of ErbB2 and ErbB3 proteins reconstitutes a high affinity receptor for heregulin. J Biol Chem 269(20):14661–14665Google Scholar
  79. 79.
    Tateishi M, Ishida T, Mitsudomi T, Kaneko S, Sugimachi K (1990) Immunohistochemical evidence of autocrine growth factors in adenocarcinoma of the human lung. Cancer Res 50(21):7077–7080Google Scholar
  80. 80.
    Umekita Y, Ohi Y, Sagara Y, Yoshida H (2000) Co-expression of epidermal growth factor receptor and transforming growth factor-α predicts worse prognosis in breast-cancer patients. Int J Cancer 89(6):484–487Google Scholar
  81. 81.
    Wilson TR, Longley DB, Johnston PG (2006) Chemoresistance in solid tumours. Ann Oncol 17(10):x315Google Scholar
  82. 82.
    Würth R, Thellung S, Bajetto A, Mazzanti M, Florio T, Barbieri F (2016) Drug-repositioning opportunities for cancer therapy: novel molecular targets for known compounds. Drug Discov Today 21(1):190–199Google Scholar
  83. 83.
    Yarden Y (2001) The EGFR family and its ligands in human cancer: signalling mechanisms and therapeutic opportunities. Eur J Cancer 37:3–8Google Scholar
  84. 84.
    Yarden Y, Pines G (2012) The ErbB network: at last, cancer therapy meets systems biology. Nat Rev Cancer 12(8):553–563Google Scholar
  85. 85.
    Zahreddine H, Borden K (2013) Mechanisms and insights into drug resistance in cancer. Front Pharmacol 4:28Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of PhysicsUniversity of FreiburgFreiburgGermany
  2. 2.Merrimack Pharmaceuticals, Inc.CambridgeUSA

Personalised recommendations