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Modelling Molecular Mechanisms of Cancer Pathogenesis: Virtual Patients, Real Opportunities

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Abstract

A combination of decades of cancer research and trillions of dollars has helped to exponentially increase our understanding of the molecular processes involved in cancer pathogenesis [1, 2] and to develop new cancer drugs. However, despite progress in diagnosing and treating cancer, these diseases remain one of the leading causes of morbidity and mortality worldwide, responsible for millions of deaths each year [3]. Moreover, we are still faced, on average, with low drug response rates, very serious treatment side effects and questionable survival benefits. In Europe alone, cancer kills around 4000 people every day [4] and costs billions per year [5]. Worldwide, the number of new cancer cases is increasing every year [4], at least partly due to rapidly ageing populations [6], with the number of new cancer cases projected to reach 25 million per year by 2030, and cure rates for many common forms of cancer stagnating [4]. Due to the high number of nonresponders to existing drugs and spiralling costs of new cancer drugs—costs have almost doubled in the last decade, resulting in a dramatic decrease in the number of new drugs—an individualised approach to the diagnosis and treatment of cancer patients is desperately required.

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References

  1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57–70.

    Article  CAS  PubMed  Google Scholar 

  2. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.

    Article  CAS  PubMed  Google Scholar 

  3. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–86.

    Article  CAS  PubMed  Google Scholar 

  4. Stewart BW, Wild CP. World cancer report. IARC Non-serial Publication. WHO Press; 2014. ISBN: 978-92-832-0429-9.

    Google Scholar 

  5. Luengo-Fernandez R, Leal J, Gray A, Sullivan R. Economic burden of cancer across the European Union: a population-based cost analysis. Lancet Oncol. 2013;14:1165–74.

    Article  PubMed  Google Scholar 

  6. United Nations, Department of Economic and Social Affairs, Population Division. World population ageing 2013. ST/ESA/SER.A/348. 2013.

    Google Scholar 

  7. Bianconi E, Piovesan A, Facchin F, Beraudi A, Casadei R, Frabetti F, Vitale L, Pelleri MC, Tassani S, Piva F, Perez-Amodio S, Strippoli P, Canaider S. An estimation of the number of cells in the human body. Ann Hum Biol. 2013;40(6):463–71. Erratum in: Ann Hum Biol. 2013;40(6):471

    Article  PubMed  Google Scholar 

  8. Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature. 2013;501:328–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Burrell RA, Swanton C. Tumour heterogeneity and the evolution of polyclonal drug resistance. Mol Oncol. 2014;8:1095–111.

    Article  CAS  PubMed  Google Scholar 

  10. Lengauer C, Kinzler KW, Vogelstein B. Genetic instabilities in human cancers. Nature. 1998;396:643–9.

    Article  CAS  PubMed  Google Scholar 

  11. International Cancer Genome Consortium, Hudson TJ, et al. International network of cancer genome projects. Nature. 2010;464:993–8.

    Article  Google Scholar 

  12. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio S, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Borresen-Dale AL, Boyault S, Burkhardt B, Butler AP, Caldas C, Davies HR, Desmedt C, Eils R, Eyfjöro JE, Foekens JA, Graeves M, Hosoda F, Huter B, Ilicic T, Imbeaud S, Imielinks M, Jäger N, Jones DTW, Jones D, Knappskog S, Kool M, Lakhani SR, Lopez-Otin C, Martin S, Munshi NC, Nakamura H, Northcott PA, Pajic M, Papaemmanuil E, Paradiso Angelo, Person JV, Puente XS, Raine K, Ramakrishna Manasa, Richardson AL, Richter J, Rosenstiel P, Schlesner M, Span PN, Teague JW, Totoki Y, Tutt A, Valdes-Mas R, van’t Veer L, Vincent-Salomon A, Waddell N, Yates LR, Australian Pancreatic Cancer Genome Initiative, ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium Zucman-Rossi J, Futreal PA, McDermott U, Lichter P, Meyerson M, Grimmond SM, Siebert R, Campo E, Shibata T, Pflister SM, Campbell PJ, Stratton MR: Signatures of Mutational Processes in Human Cancer. Nature 2013; 500 (7463):415–421.

    Google Scholar 

  13. Ciriello G, Gatza ML, Beck AH, Wilkerson MD, Rhie SK, Pastore A, Zhang H, McLellan MM, Yau CY, Kandoth C, Bowlby R, Shen H, Hayat S, Fieldhouse R, Lester SC, Tse GM, Factor RE, Collins LC, Allison KH, Chen YY, Jensen K, Johnson NB, Oesterreich S, Mills GB, Cherniack AD, Robertson G, Benz C, Sander C, Laird PW, Hoadley KA, King TA, TCGA Research Network, Perou CM. Comprehensive molecular portraits of invasive lobular breast cancer. Cell. 2015;63(2):506–19.

    Article  Google Scholar 

  14. Jäger N, Schlesner M, Jones DTW, Raffel S, Mallm JP, Junge KM, Weichenhan D, Bauer T, Ishaque N, Kool M, Northcott PA, Korshunov A, Drews RM, Koster J, Versteeg R, Richter J, Hummel M, Mack SC, Taylor MD, Witt H, Swartman B, Schulte-Bockholt D, Sultan M, Yaspo ML, Lehrach H, Hutter B, Brors B, Wolf S, Plass C, Siebert R, Trumpp A, Rippe K, Lehmann I, Lichter P, Pfister SM, Eils R. Hypermutation of the inactive X chromosome is a frequent event in cancer. Cell. 2013;155(3):567–81.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2007;2:59–77.

    PubMed  PubMed Central  Google Scholar 

  16. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.

    Article  CAS  PubMed  Google Scholar 

  17. Oda K, Matsuoka Y, Funahashi A, Kitano H. A comprehensive pathway map of epidermal growth factor receptor signaling. Mol Syst Biol. 2005;1

    Google Scholar 

  18. Purvis J, Ilango V, Radhakrishnan R. Role of network branching in eliciting differential short-term signaling responses in the hypersensitive epidermal growth factor receptor mutants implicated in lung cancer. Biotech Prog. 2008;24:540–53.

    Article  Google Scholar 

  19. Shih AJ, Purvis J, Radhakrishnan R. Molecular systems biology of ErbB1 signaling: bridging the gap through multiscale modeling and high-performance computing. Mol BioSyst. 2008;4:1151–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li F, Thiele I, Jamshidi N, Palsson BO. Identification of potential pathway mediation targets in toll-like receptor signaling. PLoS Comput Biol. 2009;5:e1000292.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bachmann J, Raue A, Schilling M, Becker V, et al. Predictive mathematical models of cancer signalling pathways. J Intern Med. 2012;271:155–65.

    Article  CAS  PubMed  Google Scholar 

  22. Cho KH, Shin SY, Lee HW, Wolkenhauer O. Investigations into the analysis and modeling of the TNF alpha-mediated NF-kappa B-signaling pathway. Genome Res. 2003;13(11):2413–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Henderson D, Ogilvie LA, Hoyle N, Keilholz U, Lange B, Lehrach H, OncoTrack Consortium. Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack. Biotechnol J. 2014;9(9):1104–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ogilvie LA, Wierling C, Kessler T, Lehrach H, Lange BM. Predictive modeling of drug treatment in the area of personalized medicine. Cancer Inform. 2015;14(Suppl. 4):95–103.

    PubMed  PubMed Central  Google Scholar 

  25. Röhr C, Kerick M, Fischer A, Kühn A, Kashofer K, Timmermann B, et al. High-throughput miRNA and mRNA sequencing of paired colorectal normal, tumor and metastasis tissues and bioinformatic modeling of miRNA-1 therapeutic applications. PLoS One. 2013;8(7):e67461.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wierling C, Kühn A, Hache H, Daskalaki A, Maschke-Dutz E, Peycheva S, et al. Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment. Mutat Res. 2012;746(2):163–70.

    Article  CAS  PubMed  Google Scholar 

  27. Wierling C, Kessler T, Ogilvie LA, Lange BM, Yaspo ML, Lehrach H. Network and systems biology: essential steps in virtualising drug discovery and development. Drug Discov Today Technol. 2015;15:33–40.

    Article  PubMed  Google Scholar 

  28. Klipp E, Liebermeister W, Wierling C, Lehrach H, Herwig R. Systems biology: a textbook. Weinheim: Wiley-VCH GmbH & Co. KgaA; 2009.

    Google Scholar 

  29. Wierling C, Herwig R, Lehrach H. Resources, standards and tools for systems biology. Brief Funct Genomic Proteomic. 2007;6(3):240–51.

    Article  CAS  PubMed  Google Scholar 

  30. Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K, Marlow S, Jane-Valbuena J, Friedrich DC, Kryukov G, Carter SL, McKenna A, Sivachenko A, Rosenberg M, Kiezun A, Voet D, Lawrence M, Lichtenstein LT, Gentry JG, Huang FW, Fostel J, Farlow D, Barbie D, Gandhi L, Lander ES, Gray SW, Joffe S, Janne P, Garber J, Macconaill L, Lindeman N, Rollins B, Kantoff P, Fisher SA, Gabriel S, Getz G, Garraway LA. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat Med. 2014;20(6):682–8.

    Article  CAS  PubMed  Google Scholar 

  31. Aran D, Sirota M, Butte AJ. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015;6:8971.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet. 2003;33:228–37.

    Article  CAS  PubMed  Google Scholar 

  33. Majewski J, Schwartzentruber J, Lalonde E, Montpetit A, Jabado N. What can exome sequencing do for you? J Med Genet. 2011;48:580–9.

    Article  CAS  PubMed  Google Scholar 

  34. Jones S, Anagnostou V, Lytle K, Parpart-Li S, Nesselbush M, Riley DR, Shukla M, Chesnick B, Kadan M, Papp E, Galens KG, Murphy D, Zhang T, Kann L, Sausen M, Angiuoli SV, Diaz Jr LA, Velculescu VE. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci Transl Med. 2015;7:283ra53.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Furney SJ, Turajlic S, Stamp G, Nohadani M, Carlisle A, Thomas JM, Hayes A, Strauss D, Gore M, van den Oord J, Larkin J, Marais R. Genome sequencing of mucosal melanomas reveals that they are driven by distinct mechanisms from cutaneous melanoma. J Pathol. 2013;230(3):261–9.

    Article  CAS  PubMed  Google Scholar 

  36. Roberts KG, Mullighan CG. Genomics in acute lymphoblastic leukaemia: insights and treatment implications. Nat Rev Clin Oncol. 2015;12(6):344–57.

    Article  CAS  PubMed  Google Scholar 

  37. Wan MW, Wang J, Gao X, Sklar J. RNA sequencing and its applications in cancer diagnosis and targeted therapy. N A J Med Sci. 2014;7(4):156–62.

    Google Scholar 

  38. Gan HK, Cvrljevic AN, Johns TG. The epidermal growth factor receptor variant III (EGFRvIII): where wild things are altered. FEBS J. 2013;280(21):5350–70.

    Article  CAS  PubMed  Google Scholar 

  39. Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science. 2005;310(5748):644–8.

    Article  CAS  PubMed  Google Scholar 

  40. Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet. 2011;12:87–98.

    Article  CAS  PubMed  Google Scholar 

  41. Carr TH, McEwen R, Dougherty B, Johnson JH, Dry JR, Lai Z, Ghazoui Z, Laing NM, Hodgson DR, Cruzalegui F, Hollingsworth SJ, Barrett JC. Defining actionable mutations for oncology therapeutic development. Nat Rev Cancer. 2016;16(5):319–29.

    Article  CAS  PubMed  Google Scholar 

  42. Wilkerson MD, Cabanski CR, Sun W, Hoadley KA, Walter V, Mose LE, Troester MA, Hammerman PS, Parker JS, Perou CM, Hayes DN. Integrated RNA and DNA sequencing improves mutation detection in low purity tumors. Nucleic Acids Res. 2014;42(13):e107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bode AM, Dong Z. Post-translational modification of p53 in tumorigenesis. Nat Rev Cancer. 2004;4:793–805.

    Article  CAS  PubMed  Google Scholar 

  44. Hitosugi T, Chen J. Post-translational modifications and the Warburg effect. Oncogene. 2014;33(34):4279–85.

    Article  CAS  PubMed  Google Scholar 

  45. Markiv A, Rambaruth NDS, Dwek MV. Beyond the genome and proteome: targeting protein modifications in cancer. Curr Opin Pharmacol. 2012;12:408–13.

    Article  CAS  PubMed  Google Scholar 

  46. Fredriksson S, Gullberg M, Jarvius J, Olsson C, Pietras K, Gústafsdóttir SM, Ostman A, Landegren U. Protein detection using proximity-dependent DNA ligation assays. Nat Biotechnol. 2002;20:473–7.

    Article  CAS  PubMed  Google Scholar 

  47. Geißler D, Stufler S, Löhmannsröben HG, Hildebrandt N. Six-color time-resolved Förster resonance energy transfer for ultrasensitive multiplexed biosensing. J Am Chem Soc. 2013;135:1102–9.

    Article  PubMed  Google Scholar 

  48. Geißler D, Charbonnière LJ, Ziessel RF, Butlin NG, Löhmannsröben HG, Hildebrandt N. Quantum dot biosensors for ultra-sensitive multiplexed diagnostics. Angew Chem Int Ed Engl. 2010;49(8):1396–401.

    Article  PubMed  Google Scholar 

  49. Morgner F, Geißler D, Stufler S, Butlin NG, Löhmannsröben HG, Hildebrandt N. A quantum-dot-based molecular ruler for multiplexed optical analysis. Angew Chem. 2010;49:7570–4.

    Article  CAS  Google Scholar 

  50. Spurrier B, Ramalingam S, Nishizuka S. Reverse-phase protein lysate microarrays for cell signaling analysis. Nat Protoc. 2008;3:1796–808.

    Article  PubMed  Google Scholar 

  51. Wegner KD, Lindén S, Jin Z, Jennings TL, el Khoulati R, van Bergen en Henegouwen PM, Hildebrandt N. Nanobodies and Nanocrystals: highly sensitive quantum dot-based homogeneous FRET-immunoassay for serum-based EGFR detection. Small. 2014;10(4):734–40.

    Article  CAS  PubMed  Google Scholar 

  52. Crosetto N, Bienko M, van Oudenaarden A. Spatially resolved transcriptomics and beyond. Nat Rev Genet. 2015;16:57–66.

    Article  CAS  PubMed  Google Scholar 

  53. Mali P, Aach J, Lee J, Levner D, Nip L, Church GM. Barcoding cells using cell-surface programmable DNA-binding domains. Nat Methods. 2013;10(5):403–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Soderberg O, Gullberg M, Jarvius M, Ridderstråle K, Leuchowius KJ, Jarvius J, Wester K, Hydbring P, Bahram F, Larsson LG, Landegren U. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nat Methods. 2006;3:995–1000.

    Article  PubMed  Google Scholar 

  55. Weibrecht I, Lundin E, Kiflemariam S, Mignardi M, Grundberg I, Larsson C, Koos B, Nilsson M, Söderberg O. In situ detection of individual mRNA molecules and protein complexes or post-translational modifications using padlock probes combined with the in situ proximity ligation assay. Nat Protoc. 2013;8:355–72.

    Article  CAS  PubMed  Google Scholar 

  56. Weibrecht I, Gavrilovic M, Lindbom L, Landegren U, Wählby C, Söderberg O. Visualising individual sequence-specific protein-DNA interactions in situ. New Biotechnol. 2012;29:589–98.

    Article  CAS  Google Scholar 

  57. Chen R, Snyder M. Systems biology: personalized medicine for the future? Curr Opin Pharmacol. 2012;12(5):623–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotechnol. 2012;29(6):613–24.

    Article  CAS  Google Scholar 

  59. Enderling H, Rejniak KA. Simulating cancer: computational models in oncology. Front Oncol. 2013;3:233.

    PubMed  PubMed Central  Google Scholar 

  60. Faratian D, Bown JL, Smith VA, Langdon SP, Harrison DJ. Cancer systems biology. Methods Mol Biol. 2010;662:245–63.

    Article  CAS  PubMed  Google Scholar 

  61. Deisboeck TS, Wang Z, Macklin P, Cristini V. Multiscale cancer modeling. Annu Rev Biomed Eng. 2011;13:127–55.

    Article  CAS  PubMed  Google Scholar 

  62. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 2014;42(Database issue):D472–7.

    Article  CAS  PubMed  Google Scholar 

  63. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;27(1):29–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. PID: the Pathway Interaction Database. Nucleic Acids Res. 2009;37(Database issue):D674–9.

    Article  CAS  PubMed  Google Scholar 

  65. Bader GD, Cary MP, Sander C. Pathguide: a pathway resource list. Nucleic Acids Res. 2006;34(Database issue):D504–6.

    Article  CAS  PubMed  Google Scholar 

  66. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C. Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 2011;39(Database issue):D685–90.

    Article  CAS  PubMed  Google Scholar 

  67. Kamburov A, Wierling C, Lehrach H, Herwig R. ConsensusPathDB—a database for integrating human functional interaction networks. Nucleic Acids Res. 2009;37(Database issue):D623–8.

    Article  CAS  PubMed  Google Scholar 

  68. Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R. ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 2011;39(Database issue):D712–7.

    Article  CAS  PubMed  Google Scholar 

  69. Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C. WikiPathways: pathway editing for the people. PLoS Biol. 2008;6(7):e184.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Pratt D, Chen J, Welker D, Rivas R, Pillich R, Rynkov V, Ono K, Miello C, Hicks L, Szalma S, Stojmirovic A, Dobrin R, Braxenthaler M, Kuentzer J, Demchak B, Ideker T. NDEx, the Network Data Exchange. Cell Syst. 2015;1(4):302–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 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, Consortium HPN-DREAM, 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. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 2016;13(4):310–8.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Yu MK, Kramer M, Dutkowski J, Srivas R, Licon K, Kreisberg J, Ng CT, Krogan N, Sharan R, Ideker T. Translation of genotype to phenotype by a hierarchy of cell subsystems. Cell Syst. 2016;2(2):77–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Prinz F, Schlange T, Asadullah K. Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov. 2011;10(9):712.

    Article  CAS  PubMed  Google Scholar 

  74. Ericsson AC, Davis JW, Spollen W, Bivens N, Givan S, Hagan CE, McIntosh M, Franklin CL. Effects of vendor and genetic background on the composition of the fecal microbiota of inbred mice. PLoS One. 2015;10(2):e0116704.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Jensen MN, Ritskes-Hoitinga M. How isoflavone levels in common rodent diets can interfere with the value of animal models and with experimental results. Lab Anim. 2007;41(1):1–18.

    Article  CAS  PubMed  Google Scholar 

  76. Rogers GB, Kozlowska J, Keeble J, Metcalfe K, Fao M, Dowd SE, Mason AJ, McGuckin MA, Bruce KD. Functional divergence in gastrointestinal microbiota in physically-separated genetically identical mice. Sci Rep. 2014;4:5437.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Baker M. Reproducibility crisis: blame it on the antibodies. Nature. 2015;521(7552):274–6.

    Article  CAS  PubMed  Google Scholar 

  78. Klipp E, Liebermeister L, Wierling C, Kowald A. Systems biology. A Textbook. Weinheim: Wiley-Blackwell; 2016.

    Google Scholar 

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Acknowledgements

The authors would like to thank their colleagues at Alacris Theranostics GmbH and the Dahlem Centre for Genome Research and Medical Systems Biology (DCGMS), for fruitful discussions and constructive criticism.

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Lehrach, H., Kessler, T., Ogilvie, L., Schütte, M., Wierling, C. (2017). Modelling Molecular Mechanisms of Cancer Pathogenesis: Virtual Patients, Real Opportunities. In: Haybaeck, J. (eds) Mechanisms of Molecular Carcinogenesis – Volume 2. Springer, Cham. https://doi.org/10.1007/978-3-319-53661-3_16

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