Omics-based Medicine and Systems Pathology

H. Tanaka

doi:10.3414/ME9307

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2010; 49(02): 173-185
DOI: 10.3414/ME9307

Special Topic – Original Articles

Schattauer GmbH

Omics-based Medicine and Systems Pathology

A New Perspective for Personalized and Predictive Medicine

H. Tanaka

¹Graduate School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan

› Author Affiliations

Abstract

Summary

Objectives: Recent important advances in the human genomics and post-genomic “omics” are now bringing about a new medical care which we call “omics-based medicine”. In this article, we investigated the development and future possibilities of omics-based medicine.

Methods: We divided the development of omics-based medicine into three generations in order to clarify the main clinical goals and characteristics of informatics method of each generation, together with its future possibilities.

Results: The first generation of omics-based medicine started with “genomic medicine” based on the inborn individual differences of genome. It has opened the study of genetic polymorphism of the diseases and promoted the personalized medication based on the pharmacogenetic/pharmacogenomic difference of the drug response. In the second generation of omics-based medicine, owing to the advances in the high-throughput technology, vast amount of the various post-genomic disease omics data containing comprehensive molecular information of diseased somatic cells has become available. It reflects the ongoing state of diseases more closely and enables the predictive medicine such as prognosis prediction of disease by applying the data-driven analysis. Finally, due to the rapidly growing knowledge about the cellular molecular network, system-level understanding of the disease, called systems pathology, becomes possible. It can fully exploit the substantial contents of disease omics and will lead to a comprehensive understanding of disease process by using model-driven analysis.

Conclusion: Omics-based medicine and systems pathology will realize a new personalized and predictive medicine.

Keywords

Omics-based medicine - systems pathology - genomics - omics - pharmacogenomics

Full Text

References

References
1 Weinstein JN. Fishing expeditions. Science 1998; 282: 628-629.
2 Weinstein JN. ‘Omic’ and hypothesis-driven research in the molecular pharmacology of cancer. Curr Opin Pharmacol 2002; 2: 361-365.
3 Knaup P, Ammenwerth E, Brandner R, Brigl B, Fischer G, Garde S, Lang E, Pilgram R, Ruderich F, Singer R, Wolff AC, Haux R, Kulikowski C. Towards clinical bioinformatics: advancing genomic medicine with informatics methods and tools. Methods Inf Med 2004; 43: 302-307.
4 Gaughan A. Bridging the divide: the need for translational informatics. Pharmacogenomics 2006; 7: 117-122.
5 Guttmacher AE, Collins FS. Genomic medicine – a primer. N Engl J Med 2002; 347: 1512-1520.
6 Kumar D. Genomic medicine: a new frontier of medicine in the twenty first century. Genomic Med 2007; 1: 3-7.
7 Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995; 270: 467-470.
8 Fodor SP, Rava RP, Huang XC, Pease AC, Holmes CP, Adams CL. Multiplexed biochemical assays with biological chips. Nature 1993; 364: 555-556.
9 Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E, Volkert TL, Wilson CJ, Bell SP, Young RA. Genome-wide location and function of DNA binding proteins. Science 2000; 290: 2306-2309.
10 Shoemaker DD, Schadt EE, Armour CD, He YD, Garrett-Engele P, McDonagh PD, Loerch PM, Leonardson A, Lum PY, Cavet G, Wu LF, Altschuler SJ, Edwards S, King J, Tsang JS, Schimmack G, Schelter JM, Koch J, Ziman M, Marton MJ, Li B, Cundiff P, Ward T, Castle J, Krolewski M, Meyer MR, Mao M, Burchard J, Kidd MJ, Dai H, Phillips JW, Linsley PS, Stoughton R, Scherer S, Boguski MS. Experimental annotation of the human genome using microarray technology. Nature 2001; 409: 922-927.
11 Xing Y, Kapur K, Wong WH. Probe selection and expression index computation of Affymetrix Exon Arrays. PLoS ONE 2006; 1: e88.
12 Tanaka K, Waki H, Ido Y, Akita S, Yoshida Y, Yoshida T. Protein and polymer analyses up to m/z 100000 by laser ionization time-of-flight mass spec-trometry. Rapid Commun Mass Spectrom 1988; 2: 151-153.
13 Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Commun Mass Spectrom 1993; 7: 576-580.
14 Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531-537.
15 Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503-511.
16 Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Herme-link HK, Smeland EB, Giltnane JM, Hurt EM, Zhao H, Averett L, Yang L, Wilson WH, Jaffe ES, Simon R, Klausner RD, Powell J, Duffey PL, Longo DL, Greiner TC, Weisenburger DD, Sanger WG, Dave BJ, Lynch JC, Vose J, Armitage JO, Montserrat E, López-Guillermo A, Grogan TM, Miller TP, LeBlanc M, Ott G, Kvaloy S, Delabie J, Holte H, Krajci P, Stokke T, Staudt LM. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002; 346: 1937-1947.
17 Tanaka H. Bioinformatics and genomics for opening new perspective for personalized care. Stud Health Technol Inform 2008; 134: 47-58.
18 Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, Mills GB, Simone C, Fish-man DA, Kohn EC, Liotta LA. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002; 359: 572-577.
19 Meani F, Pecorelli S, Liotta L, Petricoin EF. Clinical application of proteomics in ovarian cancer prevention and treatment. Mol Diagn Ther 2009; 13: 297-311.
20 Grubb RL, Deng J, Pinto PA, Mohler JL, Chinnaiyan A, Rubin M, Linehan WM, Liotta LA, Petricoin EF, Wulfkuhle JD. Pathway biomarker profiling of localized and metastatic human prostate cancer reveal metastatic and prognostic signatures. J Proteome Res 2009; 8: 3044-3054.
21 Tanaka H. System-theoretic approach to life, genome and disease. Proc of 3rd annual conference of Chemo-Bioinformatics. 2002 p 3.
22 Tanaka H, Nakaya J. Research and education for biomedical informatics at Tokyo Medical and Dental University. Yearb Med Inform. 2007 pp 157-162.
23 Kitano H. Systems biology: a brief overview. Science 2002; 295: 1662-1664.
24 Kitano H. Computational systems biology. Nature 2002; 420: 206-210.
25 Hood L, Heath JR, Phelps ME, Lin B. Systems biology and new technologies enable predictive and preventative medicine. Science 2004; 306: 640-643.
26 Auffray C, Chen Z, Hood L. Systems medicine: the future of medical genomics and healthcare. Genome Med 2009; 1: 2.
27 International human genome sequencing consortium.. Finishing the euchromatic sequence of the human genome. Nature 2004; 431: 931-945.
28 Strachan T, Andrew PR. Human molecular genetics (3rd edition). New York: Garland Publishing; 2003
29 Gusella JF, Wexler NS, Conneally PM, Naylor SL, Anderson MA, Tanzi RE, Watkins PC, Ottina K, Wallace MR, Sakaguchi AY. A polymorphic DNA marker genetically linked to Huntington’s disease. Nature 1983; 306: 234-238.
30 Balding DJ, Bishop M, Cannings C. Handbook of Statistical Genetics (3rd edition). Hoboken: Wiley; 2007
31 Huntington’s Disease Collaborative Research Group.. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 1993; 72: 971-983.
32 Hoffman EP, Brown RH, Kunkel LM. Dystrophin: the protein product of the Duchenne muscular dystrophy locus. Cell 1987; 51: 919-928.
33 Rommens JM, Iannuzzi MC, Kerem B, Drumm ML, Melmer G, Dean M, Rozmahel R, Cole JL, Kennedy D, Hidaka N. Identification of the cystic fibrosis gene: chromosome walking and jumping. Science 1989; 245: 1059-1065.
34 Schaid DJ, Sommer SS. Genotype relative risks: methods for design and analysis of candidate-gene association studies. Am J Hum Genet 1993; 53: 1114-1126.
35 Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ, Lavery T, Kouyoumjian R, Farhadian SF, Ward R, Lander ES. Linkage disequilibrium in the human genome. Nature 2001; 411: 199-204.
36 Hardy J, Singleton A. Genomewide association studies and human disease. N Engl J Med 2009; 360: 1759-1768.
37 Frayling TM. Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat Rev Genet 2007; 8: 657-662.
38 Diabetes Genetics Initiative.. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007; 316: 1331-1336.
39 The Wellcome Trust case control consortium.. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007; 447: 661-678.
40 Teo YY, Small KS, Clark TG, Kwiatkowski DP. Perturbation analysis: a simple method for filtering SNPs with erroneous genotyping in genome-wide association studies. Ann Hum Genet 2008; 72: 368-374.
41 International Hapmap consortium.. The International HapMap Project. Nature 2003; 426: 789-796.
42 Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL. The human disease network. Proc Natl Acad Sci U. S. A. 2007; 104: 8685-8690.
43 Evans WE, Relling MV. Moving towards individualized medicine with pharmacogenomics. Nature 2004; 429: 464-468.
44 Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 1998; 279: 1200-1205.
45 FDA. Guidance for Industry Pharmacogenomic Data Submissions. 2003 http://www.fda.gov/cder/guidance/5900dft.pdf.
46 Blackledge G, Averbuch S. Gefitinib (‘Iressa’, ZD1839) and new epidermal growth factor receptor inhibitors. Br J Cancer 2004; 90: 566-572.
47 Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, Harris PL, Haserlat SM, Supko JG, Haluska FG, Louis DN, Christiani DC, Settleman J, Haber DA. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 2004; 350: 2129-2139.
48 Asahina H, Yamazaki K, Kinoshita I, Sukoh N, Harada M, Yokouchi H, Ishida T, Ogura S, Kojima T, Okamoto Y, Fujita Y, Dosaka-Akita H, Isobe H, Nishimura M. A phase II trial of gefitinib as first-line therapy for advanced non-small cell lung cancer with epidermal growth factor receptor mutations. Br J Cancer 2006; 95: 998-1004.
49 Baselga J. Clinical trials of Herceptin (trastuzumab). Eur J Cancer 2001; 37 (01) S18-24.
50 Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A, Fleming T, Eiermann W, Wolter J, Pegram M, Baselga J, Norton L. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 2001; 344: 783-792.
51 Schwarz UI, Ritchie MD, Bradford Y, Li C, Dudek SM, Frye-Anderson A, Kim RB, Roden DM, Stein CM. Genetic determinants of response to warfarin during initial anticoagulation. N Engl J Med 2008; 358: 999-1008.
52 Aneesh TP, Sonal SM, Jose A, Chandrana L, Zachariaha SM. Pharmacogenomics: The Right Drug to the Right Person. J Clin Med Res 2009; 1: 191-194.
53 van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-536.
54 Sørlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lønning PE, Brown PO, Børresen-Dale AL, Botstein D. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U. S. A. 2003; 100: 8418-8423.
55 Slodkowska EA, Ross JS. MammaPrint 70-gene signature: another milestone in personalized medical care for breast cancer patients. Expert Rev Mol Diagn 2009; 9: 417-422.
56 Asad J, Jacobson AF, Estabrook A, Smith SR, Boolbol SK, Feldman SM, Osborne MP, Boachie-Adjei K, Twardzik W, Tartter PI. Does oncotype DX recurrence score affect the management of patients with early-stage breast cancer?. Am J Surg 2008; 196: 527-529.
57 Cheng JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Elledge R, Mohsin S, Osborne CK, Chamness GC, Allred DC, O’Connell P. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 2003; 362: 362-369.
58 Ayers M, Symmans WF, Stec J, Damokosh AI, Clark E, Hess K, Lecocke M, Metivier J, Booser D, Ibrahim N, Valero V, Royce M, Arun B, Whitman G, Ross J, Sneige N, Hortobagyi GN, Pusztai L. Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 2004; 22: 2284-2293.
59 Mogushi K, Nomura F, Tomonaga T, Sunaga M, Sogawa K, Tanaka H. Evaluation of feature selection and disease classification method for protein expression profile analysis using SELDI-TOF-MS. Proc. 2nd Annual Meeting of Japan Society for Clinical Proteomics 2006 p 20.
60 Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol 2008; 26: 1135-1145.
61 Trapnell C, Salzberg SL. How to map billions of short reads onto genomes. Nat Biotechnol 2009; 27: 455-457.
62 Kleemann R, Verschuren L, van Erk MJ, Nikolsky Y, Cnubben NH, Verheij ER, Smilde AK, Hendriks HF, Zadelaar S, Smith GJ, Kaznacheev V, Nikolskaya T, Melnikov A, Hurt-Camejo E, van der Greef J, van Ommen B, Kooistra T. Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis. Genome Biol. 2007; 8: R200.
63 Zhao JJ, Roberts TM, Hahn WC. Functional genetics and experimental models of human cancer. Trends Mol Med 2004; 10: 344-350.
64 Hirano T. Cytokines in autoimmune disease and chronic inflammatory proliferative disease. Cytokine Growth Factor Rev 2002; 13: 297-298.
65 Schiffrin EL. Remodeling of resistance arteries in essential hypertension and effects of antihypertensive treatment. Am J Hypertens 2004; 17: 1192-200.
66 Drummond A, Rodrigo AG. Reconstructing genealogies of serial samples under the assumption of a molecular clock using serial-sample UPGMA. Mol Biol Evol 2000; 17: 1807-1815.
67 Ren F, Ogishima S, Tanaka H. Longitudinal phylo-genetic tree of within-host viral evolution from noncontemporaneous samples: a distance-based sequential-linking method. Gene 2003; 317: 89-95.
68 Hasegawa N, Sugiura W, Shibata J, Matsuda M, Ren F, Tanaka H. Inferring within-patient HIV-1 evolutionary dynamics under anti-HIV therapy using serial virus samples with v SPA. BMC Bioinformatics 2009; 10: 360.
69 Michor F, Iwasa Y, Nowak MA. Dynamics of cancer progression. Nat Rev Cancer 2004; 4: 197-205.
70 Lee JM, Dedhar S, Kalluri R, Thompson EW. The epithelial-mesenchymal transition: new insights in signaling, development, and disease. J Cell Biol 2006; 172: 973-981.
71 Liang S, Fuhrman S, Somogyi R. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 1998; 00: 18-29.
72 Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol 2000; 7: 601-620.
73 Edwards D. Introduction to Graphical Modelling. Heidelberg: Springer; 2000
74 Butte AJ, Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000 pp 418-429.
75 Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2006; 7 (01) S7.
76 Friedman N, Koller D. Being Bayesian about network structure. Math Learn 2003; 50: 95-126.
77 Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet 2005; 37: 382-390.
78 Markowetz F, Spang R. Inferring cellular networks – a review. BMC Bioinformatics 2007; 8 (06) S5.
79 Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007; 3: 140.
80 Tanaka S, Mogushi K, Yasen M, Noguchi N, Kudo A, Kurokawa T, Nakamura N, Inazawa J, Tanaka H, Arii S. Surgical contribution to recurrence-free survival in patients with macrovascular-invasion-negative hepatocellular carcinoma. J Am Coll Surg 2009; 208: 368-374.
81 Göring HH, Curran JE, Johnson MP, Dyer TD, Charlesworth J, Cole SA, Jowett JB, Abraham LJ, Rainwater DL, Comuzzie AG, Mahaney MC, Almasy L, MacCluer JW, Kissebah AH, Collier GR, Moses EK, Blangero J. Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nat Genet 2007; 39: 1208-1216.
82 Wang E, Lenferink A, O’Connor-McCourt M. Cancer systems biology: exploring cancer-associated genes on cellular networks. Cell Mol Life Sci 2007; 64: 1752-1762.
83 Sieberts SK, Schadt EE. Moving toward a system genetics view of disease. Mamm Genome 2007; 18: 389-401.
84 Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL. The large-scale organization of metabolic networks. Nature 2000; 407: 651-654.
85 Hase T, Tanaka H, Suzuki Y, Nakagawa S, Kitano H. Structure of protein interaction networks and their implications on drug design. PLoS Comput Biol 2009; 5: e1000550.
86 Kuipers B. Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge (Artificial Intelligence). Cambridge: The MIT Press; 1994. p 13.