Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems

  • Silvio Parodi
  • Giuseppe Riccardi
  • Nicoletta Castagnino
  • Lorenzo Tortolina
  • Massimo Maffei
  • Gabriele Zoppoli
  • Alessio Nencioni
  • Alberto Ballestrero
  • Franco Patrone
Part of the Methods in Molecular Biology book series (MIMB, volume 1386)


Two different perspectives are the main focus of this book chapter: (1) A perspective that looks to the future, with the goal of devising rational associations of targeted inhibitors against distinct altered signaling-network pathways. This goal implies a sufficiently in-depth molecular diagnosis of the personal cancer of a given patient. A sufficiently robust and extended dynamic modeling will suggest rational combinations of the abovementioned oncoprotein inhibitors. The work toward new selective drugs, in the field of medicinal chemistry, is very intensive. Rational associations of selective drug inhibitors will become progressively a more realistic goal within the next 3–5 years. Toward the possibility of an implementation in standard oncologic structures of technologically sufficiently advanced countries, new (legal) rules probably will have to be established through a consensus process, at the level of both diagnostic and therapeutic behaviors.

(2) The cancer patient of today is not the patient of 5–10 years from now. How to support the choice of the most convenient (and already clinically allowed) treatment for an individual cancer patient, as of today? We will consider the present level of artificial intelligence (AI) sophistication and the continuous feeding, updating, and integration of cancer-related new data, in AI systems. We will also report briefly about one of the most important projects in this field: IBM Watson US Cancer Centers. Allowing for a temporal shift, in the long term the two perspectives should move in the same direction, with a necessary time lag between them.

Key words

Cancer genomics Signaling-network pathways Individual cancer patient Oncoprotein inhibitors Rational associations of targeted inhibitors New clinical trial designs Systems medicine Decision-support systems Artificial intelligence IBM Watson 


  1. 1.
    Hoadley KA, Yau C, Wolf DM et al (2014) Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158:929–944PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Perou CM, Sørlie T, Eisen MB et al (2000) Molecular portraits of human breast tumors. Nature 406:747–752CrossRefPubMedGoogle Scholar
  3. 3.
    Zack TI, Schumacher SE, Carter SL et al (2013) Pan-cancer patterns of somatic copy number alteration. Nat Genet 45:1134–1140PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Lacombe D, Tejpar S, Salgado R et al (2014) European perspective for effective cancer drug development. Nat Rev Clin Oncol 11:492–498CrossRefPubMedGoogle Scholar
  5. 5.
    Zardavas D, Maetens M, Irrthum A et al (2014) The AURORA initiative for metastatic breast cancer. Br J Cancer 111:1881–1887PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Garraway LA, Lander ES (2013) Lessons from the cancer genome. Cell 153:17–37CrossRefPubMedGoogle Scholar
  7. 7.
    Amirkhah R, Schmitz U, Linnebacher M et al (2014) MicroRNA-mRNA interactions in colorectal cancer and their role in tumor progression. Genes Chromosom Cancer 54:129–141CrossRefGoogle Scholar
  8. 8.
    Vogelstein B, Papadopoulos N, Velculescu VE et al (2013) Cancer genome landscapes. Science 339:1546–1558PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Catalogue of Somatic Mutation in Cancer (COSMIC).
  10. 10.
    Futreal PA, Coin L, Marshall M et al (2004) A census of human cancer genes. Nat Rev Cancer 4:177–183PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    The Cancer Genome Atlas (TCGA).
  12. 12.
    International Cancer Genome Consortium (ICGC). The ICGC Data Portal.
  13. 13.
    The cBioPortal for Cancer Genomics.
  14. 14.
    The Cancer Genomics Hub (CGHub).
  15. 15.
  16. 16.
    Griffith M, Griffith OL, Coffman AC et al (2013) DGIdb: mining the druggable genome. Nat Methods 10:1209–1210CrossRefPubMedGoogle Scholar
  17. 17.
    The Drug Gene Interaction Database.
  18. 18.
    The Genomics of Drug Sensitivity in Cancer (GDSC).
  19. 19.
    Brownstein CA, Beggs AH, Homer N et al (2014) An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biol 15:R53PubMedCentralCrossRefPubMedGoogle Scholar
  20. 20.
    Stahel R, Bogaerts J, Ciardiello F, de Ruysscher D et al. (2014) Optimising translational oncology in clinical practice: strategies to accelerate progress in drug development. Cancer Treat Rev. pii: S0305-7372 (14) 00209-6Google Scholar
  21. 21.
  22. 22.
    Ferrucci D, Brown E, Chu-Carroll J et al (2010) Building Watson: an overview of the DeepQA project. AI Mag 31:59–79Google Scholar
  23. 23.
    Moschitti A, Chu-Carroll J, Patwardhan S et al. (2011) Using syntactic and semantic structural kernels for classifying definition questions in jeopardy!. Proceedings of the conference on empirical methods in natural language processing. pp 712–724Google Scholar
  24. 24.
    Kinzler KW, Vogelstein B (1997) Cancer-susceptibility genes Gatekeepers and caretakers. Nature 386:761–763CrossRefPubMedGoogle Scholar
  25. 25.
    Vogelstein B, Kinzler KW (2004) Cancer genes and the pathways they control. Nat Med 10:789–799CrossRefPubMedGoogle Scholar
  26. 26.
    Marx V (2014) Cancer genomes: discerning drivers from passengers. Nat Methods 11:375–379CrossRefGoogle Scholar
  27. 27.
    Lawrence MS, Lawrence MS, Stojanov P et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214–218PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Lawrence MS, Stojanov P, Mermel CH et al (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505:495–501PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J et al (2013) IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods 10:1081–1082CrossRefPubMedGoogle Scholar
  30. 30.
    IntOGen-mutations platform.
  31. 31.
    Martelotto LG, Ng C, De Filippo MR et al (2014) Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. Genome Biol 15:484PubMedCentralCrossRefPubMedGoogle Scholar
  32. 32.
    Yeang CH, McCormick F, Levine A (2008) Combinatorial patterns of somatic gene mutations in cancer. FASEB J 22:2605–2622CrossRefPubMedGoogle Scholar
  33. 33.
    Nik-Zainal S, Alexandrov LB, Wedge DC et al (2012) Mutational processes molding the genomes of 21 breast cancers. Cell 149:979–993PubMedCentralCrossRefPubMedGoogle Scholar
  34. 34.
    Alexandrov LB, Nik-Zainal S, Wedge DC et al (2013) Signatures of mutational processes in human cancer. Nature 500:415–421PubMedCentralCrossRefPubMedGoogle Scholar
  35. 35.
    Weinstein IB, Joe AK (2006) Mechanisms of disease: oncogene addiction – a rationale for molecular targeting in cancer therapy. Nat Clin Pract Oncol 3:448–457CrossRefPubMedGoogle Scholar
  36. 36.
    Druker BJ, Talpaz M, Resta DJ et al (2001) Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N Engl J Med 344:1031–1037CrossRefPubMedGoogle Scholar
  37. 37.
    Shaw AT, Kim DW, Nakagawa K et al (2013) Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med 368:2385–2394CrossRefPubMedGoogle Scholar
  38. 38.
    Chapman PB, Hauschild A, Robert C et al (2011) Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med 364:2507–2516PubMedCentralCrossRefPubMedGoogle Scholar
  39. 39.
    Mok TS, Wu YL, Thongprasert S et al (2009) Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 361:947–957CrossRefPubMedGoogle Scholar
  40. 40.
    De Roock W, Claes B, Bernasconi D et al (2010) Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol 11:753–762CrossRefPubMedGoogle Scholar
  41. 41.
    Grothey A, Lenz HJ (2012) Explaining the unexplainable: EGFR antibodies in colorectal cancer. J Clin Oncol 30:1735–1737CrossRefPubMedGoogle Scholar
  42. 42.
    Samalin E, Bouché O, Thézenas S et al (2014) Sorafenib and irinotecan (NEXIRI) as second- or later-line treatment for patients with metastatic colorectal cancer and KRAS-mutated tumours: a multicentre Phase I/II trial. Br J Cancer 110:1148–1154PubMedCentralCrossRefPubMedGoogle Scholar
  43. 43.
    Shih T, Lindley C (2006) Bevacizumab: an angiogenesis inhibitor for the treatment of solid malignancies. Clin Ther 28:1779–1802CrossRefPubMedGoogle Scholar
  44. 44.
    Grothey A, Van Cutsem E, Sobrero A et al (2013) Regorafenib monotherapy for previously treated metastatic colorectal cancer (CORRECT): an international, multicentre, randomised, placebo-controlled, phase 3 trial. Lancet 381:303–312CrossRefPubMedGoogle Scholar
  45. 45.
    Sun C, Hobor S, Bertotti A et al (2014) Intrinsic resistance to MEK inhibition in KRAS mutant lung and colon cancer through transcriptional induction of ERBB3. Cell Rep 7:86–93CrossRefPubMedGoogle Scholar
  46. 46.
    Ng K, Tabernero J, Hwang J et al (2013) Phase II study of everolimus in patients with metastatic colorectal adenocarcinoma previously treated with bevacizumab-, fluoropyrimidine-, oxaliplatin-, and irinotecan-based regimens. Clin Cancer Res 19:3987–3995PubMedCentralCrossRefPubMedGoogle Scholar
  47. 47.
    Iyer G, Hanrahan AJ, Milowsky MI et al (2012) Genome sequencing identifies a basis for everolimus sensitivity. Science 338:221PubMedCentralCrossRefPubMedGoogle Scholar
  48. 48.
    Integrating personalised medicine into EU strategy. EAPM annual conference report Bibliothéque Solvay and the European Parliament, Brussels 9–10 September, 2014.
  49. 49.
    Pal I, Mandal M (2012) PI3K and Akt as molecular targets for cancer therapy: current clinical outcomes. Acta Pharmacol Sin 33:1441–1458PubMedCentralCrossRefPubMedGoogle Scholar
  50. 50.
    Zhao Y, Aguilar A, Bernard D et al (2015) (2014) Small-molecule inhibitors of the MDM2–p53 protein-protein interaction (MDM2 inhibitors) in clinical trials for cancer treatment. J Med Chem 8(3):1038–52CrossRefGoogle Scholar
  51. 51.
    Huang SM, Mishina YM, Liu S et al (2009) Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature 461:614–620CrossRefPubMedGoogle Scholar
  52. 52.
    FDA Public Workshop. Innovations in breast cancer drug development – next generation oncology trials. Breast Cancer Workshop. October 21, 2014. Session 1 improving targeted drug development for “small” populations with genomic.
  53. 53.
    Lillie EO, Patay B, Diamant J et al (2011) The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med 8:161–173PubMedCentralCrossRefPubMedGoogle Scholar
  54. 54.
    Zauderer MG, Gucalp A, Epstein AS et al (2014) Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network. Journal of Clinical Oncology 32(15 suppl):e17653, 2014 ASCO Annual Meeting AbstractsGoogle Scholar
  55. 55.
    Rodin M. IBM Watson: Transforming expertise in the new era of computing. Presented at Mayo Clinic Transform 2014, Washington, DC/San Francisco, Sept 7–9, 2014.
  56. 56.
  57. 57.
    Crystal AS, Shaw AT, Sequist LV et al (2014) Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346:1480–1486PubMedCentralCrossRefPubMedGoogle Scholar
  58. 58.
    Cancer Cell Line Encyclopedia.
  59. 59.
    Zauderer MG, Gucalp A, Epstein AS, Seidman AD, Caroline A, Granovsky S, Julia F, Keesing J, Lewis S, Co H, Petri J, Megerian M, Eggebraaten T, Bach P, Kris MG, Tortolina L, Duffy DJ, Maffei M et al (2015) Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies. Oncotarget 10:5041–5058Google Scholar
  60. 60.
    Castagnino N, Tortolina L, Balbi A et al (2010) Dynamic simulations of pathways downstream of ERBB-family, including mutations and treatments. Concordance with experimental results. Curr Cancer Drug Targets 10:737–757CrossRefPubMedGoogle Scholar
  61. 61.
    Tortolina L, Castagnino N, De Ambrosi C et al (2012) A multi-scale approach to colorectal cancer: from a biochemical-interaction signaling-network level, to multi-cellular dynamics of malignant transformation. Interplay with mutations and onco-protein inhibitor drugs. Curr Cancer Drug Targets 12:339–355CrossRefPubMedGoogle Scholar
  62. 62.
    De Ambrosi C, Barla A, Tortolina L et al (2013) Parameter space exploration within dynamic simulations of signaling networks. Math Biosci Eng 10:103–120CrossRefPubMedGoogle Scholar
  63. 63.
    Kohn KW (1999) Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Mol Biol Cell 10:2703–2734PubMedCentralCrossRefPubMedGoogle Scholar
  64. 64.
    Aladjem M.I., Pasa S., Parodi S. et al. (2004) Molecular interaction maps--a diagrammatic graphical language for bioregulatory networks. Sci STKE 2004(222):pe8.Google Scholar
  65. 65.
    Kohn KW, Aladjem MI, Weinstein JN et al (2006) Molecular interaction maps of bioregulatory networks: a general rubric for systems biology. Mol Biol Cell 17:1–13PubMedCentralCrossRefPubMedGoogle Scholar
  66. 66.
  67. 67.
    Poliseno L, Salmena L, Zhang J et al (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465:1033–1038PubMedCentralCrossRefPubMedGoogle Scholar
  68. 68.
    Tay Y, Kats L, Salmena L et al (2011) Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell 147:344–357PubMedCentralCrossRefPubMedGoogle Scholar
  69. 69.
    Song MS, Salmena L, Pandolfi PP (2012) The functions and regulation of the PTEN tumour suppressor. Nat Rev Mol Cell Biol 13:283–296PubMedGoogle Scholar
  70. 70.
    Snedecor GW, Cochran WG (1967) Statistical methods 1967. Blackwell, Ames, IAGoogle Scholar
  71. 71.
  72. 72.
    Misale S, Arena S, Lamba S et al (2014) Blockade of EGFR and MEK intercepts heterogeneous mechanisms of acquired resistance to anti-EGFR therapies in colorectal cancer. Sci Transl Med 6(224):224ra26CrossRefPubMedGoogle Scholar
  73. 73.
  74. 74.
    The I-SPY 2 TRIAL – Investigation of serial studies to predict your therapeutic response with imaging and molecular analysis 2.
  75. 75.
    The NCI Molecular Analysis for Therapy Choice (MATCH) program.
  76. 76.
  77. 77.
    Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795CrossRefPubMedGoogle Scholar
  78. 78.
    Blanpain C (2013) Tracing the cellular origin of cancer. Nat Cell Biol 15:126–134CrossRefPubMedGoogle Scholar
  79. 79.
    Schmitz U, Wolkenhauer O (eds) (2016) Systems medicine methods and protocols: methods in molecular biology, vol 1386. Springer, New YorkGoogle Scholar
  80. 80.
    Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  81. 81.
    McCarthy J (1963) Programming with common sense. Defense Technical Information Center, Washington, DCGoogle Scholar
  82. 82.
    Shortliffe EH (1974) MYCIN: a rule based computer program for advising physicians regarding antimicrobial therapy selection. PhD dissertation in Medical Information Sciences. Stanford UniversityGoogle Scholar
  83. 83.
    Rabiner L (1989) A tutorial on hidden Markov Models and selected applications in speech recognition. Proc IEEE 77:257–286CrossRefGoogle Scholar
  84. 84.
    Bishop CM (2006) Pattern recognition and machine learning. Springer, New YorkGoogle Scholar
  85. 85.
    Jurafsky D, James H (2000) Speech and language processing an introduction to natural language processing, computational linguistics, and speech. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  86. 86.
    Gokhan T, De Mori R (2011) Spoken language understanding: systems for extracting semantic information from speech. John Wiley, New YorkGoogle Scholar
  87. 87.
    Narayanan S, Panayiotis GG (2013) Behavioral signal processing: deriving human behavioral informatics from speech and language. Proc IEEE 101:1203–1233CrossRefGoogle Scholar
  88. 88.
  89. 89.
    Sledge GW Jr, Miller RS, Hauser R (2013) CancerLinQ and the future of cancer care. Am Soc Clin Oncol Educ Book. pp 430-434Google Scholar
  90. 90.
    Schilsky RL, Michels DL, Kearbey AH et al (2014) Building a rapid learning health care system for oncology: the regulatory framework of CancerLinQ. J Clin Oncol 32:2373–2379CrossRefPubMedGoogle Scholar
  91. 91.
    Merolla PA, Arthur JV, Alvarez-Icaza R et al (2014) Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345:668–673CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Silvio Parodi
    • 1
  • Giuseppe Riccardi
    • 2
  • Nicoletta Castagnino
    • 1
  • Lorenzo Tortolina
    • 1
  • Massimo Maffei
    • 1
  • Gabriele Zoppoli
    • 1
  • Alessio Nencioni
    • 1
  • Alberto Ballestrero
    • 1
  • Franco Patrone
    • 1
  1. 1.Department of Internal Medicine (DIMI)Genoa UniversityGenoaItaly
  2. 2.Signals and Interactive Systems lab, Department of Engineering and Information ScienceTrento UniversityTrentoItaly

Personalised recommendations