Systems medicine in oncology: Signaling network modeling and new-generation decision-support systems

Methods in Molecular Biology

Volumn 1386, Issue , 2016, Pages 181-219

  Parodi, Silvio ^a   Riccardi, Giuseppe ^a   Castagnino, Nicoletta ^a   Tortolina, Lorenzo ^a   Maffei, Massimo ^a   Zoppoli, Gabriele ^a   Nencioni, Alessio ^a   Ballestrero, Alberto ^a   Patrone, Franco ^a  

^a NONE

Author keywords

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

Indexed keywords

ARTIFICIAL INTELLIGENCE; CANCER PATIENT; CANCER THERAPY; CONSENSUS; DECISION SUPPORT SYSTEM; HUMAN; MODEL; MOLECULAR DIAGNOSIS; ONCOLOGY; PATIENT DECISION MAKING; BIOLOGICAL MODEL; BIOLOGY; CLINICAL DECISION SUPPORT SYSTEM; COMPUTER SIMULATION; GENETIC DATABASE; METABOLISM; METHODOLOGY; NEOPLASMS; PERSONALIZED MEDICINE; PROCEDURES; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, GENETIC; DECISION SUPPORT SYSTEMS, CLINICAL; HUMANS; MEDICAL ONCOLOGY; MODELS, BIOLOGICAL; NEOPLASMS; PRECISION MEDICINE; RESEARCH DESIGN; SIGNAL TRANSDUCTION; SYSTEMS BIOLOGY;

EID: 84950300744     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-3283-2_10     Document Type: Chapter

References (91)

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 158:929-944
2. Perou CM, Sorlie T, Eisen MB et al (2000) Molecular portraits of human breast tumors. Nature 406:747-752
3. Zack TI, Schumacher SE, Carter SL et al (2013) Pan-cancer patterns of somatic copy number alteration. Nat Genet 45:1134-1140
4. Lacombe D, Tejpar S, Salgado R et al (2014) European perspective for effective cancer drug development. Nat Rev Clin Oncol 11:492-498
5. Zardavas D, Maetens M, Irrthum A et al (2014) The AURORA initiative for metastatic breast cancer. Br J Cancer 111:1881-1887
6. Garraway LA, Lander ES (2013) Lessons from the cancer genome. Cell 153:17-37
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 54:129-141
8. Vogelstein B, Papadopoulos N, Velculescu VE et al (2013) Cancer genome landscapes. Science 339:1546-1558
9. Catalogue of Somatic Mutation in Cancer (COSMIC). http://www.sanger.ac.uk/genetics/CPG/cosmic
10. . Futreal PA, Coin L, Marshall M et al (2004) A census of human cancer genes. Nat Rev Cancer 4:177-183
11. The Cancer Genome Atlas (TCGA). http://cancergenome.nih.gov/
12. . International Cancer Genome Consortium (ICGC). The ICGC Data Portal. https://icgc.org/
13. The cBioPortal for Cancer Genomics. http://www.cbioportal.org/public-portal/
14. The Cancer Genomics Hub (CGHub). https://cghub.ucsc.edu/
15. The Tumor Portal. http://cancergenome.broadinstitute.org/
16. Griffith M, Griffith OL, Coffman AC et al (2013) DGIdb: mining the druggable genome. Nat Methods 10:1209-1210
17. The Drug Gene Interaction Database. http://dgidb.genome.wustl.edu/
18. The Genomics of Drug Sensitivity in Cancer (GDSC). http://www.cancerrxgene.org/
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: 53
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) 00209-6
21. Watson IBM. http://www.ibm.com/smarter-planet/us/en/ibmwatson/20
22. Ferrucci D, Brown E, Chu-Carroll J et al (2010) Building Watson: an overview of the DeepQA project. AI Mag 31:59-79
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 712-724
24. . Kinzler KW, Vogelstein B (1997) Cancer-susceptibility genes Gatekeepers and caretakers. Nature 386:761-763
25. . Vogelstein B, Kinzler KW (2004) Cancer genes and the pathways they control. Nat Med 10:789-799
26. . Marx V (2014) Cancer genomes: discerning drivers from passengers. Nat Methods 11:375-379
27. Lawrence MS, Lawrence MS, Stojanov P et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214-218
28. . Lawrence MS, Stojanov P, Mermel CH et al (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505:495-501
29. . Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J et al (2013) IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods 10:1081-1082
30. IntOGen-mutations platform. http://www.intogen.org/mutations/
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 15:484
32. . Yeang CH, McCormick F, Levine A (2008) Combinatorial patterns of somatic gene muta­tions in cancer. FASEB J 22:2605-2622
33. . Nik-Zainal S, Alexandrov LB, Wedge DC et al (2012) Mutational processes molding the genomes of 21 breast cancers. Cell 149:979-993
34. . Alexandrov LB, Nik-Zainal S, Wedge DC et al (2013) Signatures of mutational processes in human cancer. Nature 500:415-421
35. . Weinstein IB, Joe AK (2006) Mechanisms of disease: oncogene addiction - a rationale for molecular targeting in cancer therapy. Nat Clin Pract Oncol 3:448-457
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 344:1031-1037
37. . Shaw AT, Kim DW, Nakagawa K et al (2013) Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med 368:2385-2394
38. . Chapman PB, Hauschild A, Robert C et al (2011) Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med 364:2507-2516
39. Mok TS, Wu YL, Thongprasert S et al (2009) Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 361:947-957
40. . De Roock W, Claes B, Bernasconi D et al (2010) Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetux-imab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol 11:753-762
41. . Grothey A, Lenz HJ (2012) Explaining the unexplainable: EGFR antibodies in colorectal cancer. J Clin Oncol 30:1735-1737
42. . Samalin E, Bouché O, Thézenas S et al (2014) Sorafenib and irinotecan (NEXIRI) as second-or later-line treatment for patients with meta-static colorectal cancer and KRAS-mutated tumours: a multicentre Phase I/II trial. Br J Cancer 110:1148-1154
43. . Shih T, Lindley C (2006) Bevacizumab: an angiogenesis inhibitor for the treatment of solid malignancies. Clin Ther 28:1779-1802
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 381:303-312
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 7:86-93
46. . Ng K, Tabernero J, Hwang J et al (2013) Phase II study of everolimus in patients with meta-static colorectal adenocarcinoma previously treated with bevacizumab-, fluoropyrimidine-, oxaliplatin-, and irinotecan-based regimens. Clin Cancer Res 19:3987-3995
47. . Iyer G, Hanrahan AJ, Milowsky MI et al (2012) Genome sequencing identifies a basis for evero-limus sensitivity. Science 338:221
48. Integrating personalised medicine into EU strategy. EAPM annual conference report Bibliothéque Solvay and the European Parliament, Brussels 9-10 September, 2014. http://euapm.eu/wp-content/uploads/2012/07/EAPM-Annual-Conf-Report-Integrating-Personalised-Medicine-into-the-EU-Health-Strategy.pdf
49. . Pal I, Mandal M (2012) PI3K and Akt as molecular targets for cancer therapy: current clinical outcomes. Acta Pharmacol Sin 33:1441-1458
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):1038-1052
51. . Huang SM, Mishina YM, Liu S et al (2009) Tankyrase inhibition stabilizes axin and antago­nizes Wnt signalling. Nature 461:614-620
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. http://www.fda.gov/Drugs/NewsEvents/ucm410332.htm
53. . Lillie EO, Patay B, Diamant J et al (2011) The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med 8:161-173
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): 17653, 2014 ASCO Annual Meeting Abstracts
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. www.mayo.edu/transform/talks/2014/ibm-watson-transforming-expertise-in-the-new-era-of-computing
56. ClinicalTrials.gov. https://clinicaltrials.gov/
57. Crystal AS, Shaw AT, Sequist LV et al (2014) Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346:1480-1486
58. Cancer Cell Line Encyclopedia. http://www.broadinstitute.org/ccle/home
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 10:5041-5058
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 10:737-757
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 12:339-355
62. . De Ambrosi C, Barla A, Tortolina L et al (2013) Parameter space exploration within dynamic simulations of signaling networks. Math Biosci Eng 10:103-120
63. . Kohn KW (1999) Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Mol Biol Cell 10:2703-2734
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):p8.
65. . Kohn KW, Aladjem MI, Weinstein JN et al (2006) Molecular interaction maps of bioregu-latory networks: a general rubric for systems biology. Mol Biol Cell 17:1-13
66. MATLAB. http://www.mathworks.com/products/simbiology/?BB=1
67. Poliseno L, Salmena L, Zhang J et al (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465:1033-1038
68. Tay Y, Kats L, Salmena L et al (2011) Codingindependent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell 147:344-357
69. . Song MS, Salmena L, Pandolfi PP (2012) The functions and regulation of the PTEN tumour suppressor. Nat Rev Mol Cell Biol 13:283-296
70. . Snedecor GW, Cochran WG (1967) Statistical methods 1967. Blackwell, Ames, IA
71. Statistical inference. http://en.wikipedia.org/wiki/Statistical_inference
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):224-226
73. Cell Miner. http://discover.nci.nih.gov/cellminer/home.do
74. The I-SPY 2 TRIAL - Investigation of serial studies to predict your therapeutic response with imaging and molecular analysis 2. http://ispy2.org/
75. The NCI Molecular Analysis for Therapy Choice (MATCH) program. http://www.can-cer.gov/clinicaltrials/noteworthy-trials/match
76. SAFIR02_Breast. https://clinicaltrials.gov/ct2/show/NCT02299999?term=Safir02& rank=2
77. Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793-795
78. . Blanpain C (2013) Tracing the cellular origin of cancer. Nat Cell Biol 15:126-134
79. Schmitz U, Wolkenhauer O (eds) (2016) Systems medicine methods and protocols: methods in molecular biology, vol 1386. Springer, New York
80. . Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice-Hall, Englewood Cliffs, NJ
81. McCarthy J (1963) Programming with common sense. Defense Technical Information Center, Washington, DC
82. . Shortliffe EH (1974) MYCIN: a rule based computer program for advising physicians regarding antimicrobial therapy selection. PhD dissertation in Medical Information Sciences. Stanford University
83. . Rabiner L (1989) A tutorial on hidden Markov Models and selected applications in speech recognition. Proc IEEE 77:257-286
84. . Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
85. Jurafsky D, James H (2000) Speech and language processing an introduction to natural language processing, computational linguistics, and speech. Prentice-Hall, Englewood Cliffs, NJ
86. . Gokhan T, De Mori R (2011) Spoken language understanding: systems for extracting semantic information from speech. John Wiley, New York
87. Narayanan S, Panayiotis GG (2013) Behavioral signal processing: deriving human behavioral informatics from speech and language. Proc IEEE 101:1203-1233
88. Mayo Clinic partners with IBM’s Watson to improve clinical trial patient selection. http://www.healio.com/endocrinology/practice-management/news/online/%7B193f1642-342d-492f-9be3-0e447becbf02%7D/mayo-clinic-partners-with-ibms-watson-to-improve-clinical-trial-patient-selection
89. Sledge GW Jr, Miller RS, Hauser R (2013) CancerLinQ and the future of cancer care. Am Soc Clin Oncol Educ Book. pp 430-434
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: 2373-2379
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 345:668-673