Abstract
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.
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References
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
Perou CM, Sørlie T, Eisen MB et al (2000) Molecular portraits of human breast tumors. Nature 406:747–752
Zack TI, Schumacher SE, Carter SL et al (2013) Pan-cancer patterns of somatic copy number alteration. Nat Genet 45:1134–1140
Lacombe D, Tejpar S, Salgado R et al (2014) European perspective for effective cancer drug development. Nat Rev Clin Oncol 11:492–498
Zardavas D, Maetens M, Irrthum A et al (2014) The AURORA initiative for metastatic breast cancer. Br J Cancer 111:1881–1887
Garraway LA, Lander ES (2013) Lessons from the cancer genome. Cell 153:17–37
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
Vogelstein B, Papadopoulos N, Velculescu VE et al (2013) Cancer genome landscapes. Science 339:1546–1558
Catalogue of Somatic Mutation in Cancer (COSMIC). http://www.sanger.ac.uk/genetics/CPG/cosmic
Futreal PA, Coin L, Marshall M et al (2004) A census of human cancer genes. Nat Rev Cancer 4:177–183
The Cancer Genome Atlas (TCGA). http://cancergenome.nih.gov/
International Cancer Genome Consortium (ICGC). The ICGC Data Portal. https://icgc.org/
The cBioPortal for Cancer Genomics. http://www.cbioportal.org/public-portal/
The Cancer Genomics Hub (CGHub). https://cghub.ucsc.edu/
The Tumor Portal. http://cancergenome.broadinstitute.org/
Griffith M, Griffith OL, Coffman AC et al (2013) DGIdb: mining the druggable genome. Nat Methods 10:1209–1210
The Drug Gene Interaction Database. http://dgidb.genome.wustl.edu/
The Genomics of Drug Sensitivity in Cancer (GDSC). http://www.cancerrxgene.org/
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:R53
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
Watson IBM. http://www.ibm.com/smarterplanet/us/en/ibmwatson/20
Ferrucci D, Brown E, Chu-Carroll J et al (2010) Building Watson: an overview of the DeepQA project. AI Mag 31:59–79
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
Kinzler KW, Vogelstein B (1997) Cancer-susceptibility genes Gatekeepers and caretakers. Nature 386:761–763
Vogelstein B, Kinzler KW (2004) Cancer genes and the pathways they control. Nat Med 10:789–799
Marx V (2014) Cancer genomes: discerning drivers from passengers. Nat Methods 11:375–379
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
Lawrence MS, Stojanov P, Mermel CH et al (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505:495–501
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
IntOGen-mutations platform. http://www.intogen.org/mutations/
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
Yeang CH, McCormick F, Levine A (2008) Combinatorial patterns of somatic gene mutations in cancer. FASEB J 22:2605–2622
Nik-Zainal S, Alexandrov LB, Wedge DC et al (2012) Mutational processes molding the genomes of 21 breast cancers. Cell 149:979–993
Alexandrov LB, Nik-Zainal S, Wedge DC et al (2013) Signatures of mutational processes in human cancer. Nature 500:415–421
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
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
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
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
Mok TS, Wu YL, Thongprasert S et al (2009) Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 361:947–957
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 11:753–762
Grothey A, Lenz HJ (2012) Explaining the unexplainable: EGFR antibodies in colorectal cancer. J Clin Oncol 30:1735–1737
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 110:1148–1154
Shih T, Lindley C (2006) Bevacizumab: an angiogenesis inhibitor for the treatment of solid malignancies. Clin Ther 28:1779–1802
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
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
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 19:3987–3995
Iyer G, Hanrahan AJ, Milowsky MI et al (2012) Genome sequencing identifies a basis for everolimus sensitivity. Science 338:221
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
Pal I, Mandal M (2012) PI3K and Akt as molecular targets for cancer therapy: current clinical outcomes. Acta Pharmacol Sin 33:1441–1458
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):1038–52
Huang SM, Mishina YM, Liu S et al (2009) Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature 461:614–620
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
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
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 Abstracts
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
ClinicalTrials.gov. https://clinicaltrials.gov/
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
Cancer Cell Line Encyclopedia. http://www.broadinstitute.org/ccle/home
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
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
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
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
Kohn KW (1999) Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Mol Biol Cell 10:2703–2734
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.
Kohn KW, Aladjem MI, Weinstein JN et al (2006) Molecular interaction maps of bioregulatory networks: a general rubric for systems biology. Mol Biol Cell 17:1–13
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
Tay Y, Kats L, Salmena L et al (2011) Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell 147:344–357
Song MS, Salmena L, Pandolfi PP (2012) The functions and regulation of the PTEN tumour suppressor. Nat Rev Mol Cell Biol 13:283–296
Snedecor GW, Cochran WG (1967) Statistical methods 1967. Blackwell, Ames, IA
Statistical inference. http://en.wikipedia.org/wiki/Statistical_inference
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):224ra26
Cell Miner. http://discover.nci.nih.gov/cellminer/home.do
The I-SPY 2 TRIAL – Investigation of serial studies to predict your therapeutic response with imaging and molecular analysis 2. http://ispy2.org/
The NCI Molecular Analysis for Therapy Choice (MATCH) program. http://www.cancer.gov/clinicaltrials/noteworthy-trials/match
SAFIR02_Breast. https://clinicaltrials.gov/ct2/show/NCT02299999?term=Safir02&rank=2
Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795
Blanpain C (2013) Tracing the cellular origin of cancer. Nat Cell Biol 15:126–134
Schmitz U, Wolkenhauer O (eds) (2016) Systems medicine methods and protocols: methods in molecular biology, vol 1386. Springer, New York
Russell S, Norvig P (1995) Artificial intelligence: a modern approach. Prentice-Hall, Englewood Cliffs, NJ
McCarthy J (1963) Programming with common sense. Defense Technical Information Center, Washington, DC
Shortliffe EH (1974) MYCIN: a rule based computer program for advising physicians regarding antimicrobial therapy selection. PhD dissertation in Medical Information Sciences. Stanford University
Rabiner L (1989) A tutorial on hidden Markov Models and selected applications in speech recognition. Proc IEEE 77:257–286
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Jurafsky D, James H (2000) Speech and language processing an introduction to natural language processing, computational linguistics, and speech. Prentice-Hall, Englewood Cliffs, NJ
Gokhan T, De Mori R (2011) Spoken language understanding: systems for extracting semantic information from speech. John Wiley, New York
Narayanan S, Panayiotis GG (2013) Behavioral signal processing: deriving human behavioral informatics from speech and language. Proc IEEE 101:1203–1233
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
Sledge GW Jr, Miller RS, Hauser R (2013) CancerLinQ and the future of cancer care. Am Soc Clin Oncol Educ Book. pp 430-434
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
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
Acknowledgments
This work was supported by the Italian Ministry of Economic Development “Industry 2015—Made in Italy” (MI01_00424) (A.B., F.P., S.P.) (2011–2014); Compagnia di San Paolo (1471 SD/CC N.2009.1822) (2011–2013) (2013.0927 ID ROL 4195); fellowship to M. M., Carige Foundation (2012); Start-Up grant AIRC #6108, Italian Ministry of Health grant GR-2008-1135635 (G.Z.); and FP7 project PANACREAS #256986 (A.N.).
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Parodi, S. et al. (2016). Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems. In: Schmitz, U., Wolkenhauer, O. (eds) Systems Medicine. Methods in Molecular Biology, vol 1386. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3283-2_10
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