Abstract
Understanding the molecular mechanisms underpinning prognosis and response to therapy of individuals suffering from cancer increasingly requires integrated and systematic approaches. Molecular-based strategies to more effectively prevent, diagnose, and treat cancer are seen as the future goal of oncology research. Although altered phenotypes can reliably be associated with altered gene functions, the systematic analysis of phenotypes relationships to study cancer biology remains nascent. The completion of the Human Genome Project has made possible high-throughput approaches such as the Cancer Genome Atlas to accelerate phenomics research. However, these approaches still face important challenges. In this chapter, we review these challenges, introduce current research efforts in the field, and highlight the importance of computational approaches to conduct large-scale phenomic studies.
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References
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29
Ball CA, Sherlock G, Brazma A (2004) Funding high-throughput data sharing. Nat Biotechnol 22:1179–1183
Biesecker LG (2005) Mapping phenotypes to language: a proposal to organize and standardize the clinical descriptions of malformations. Clin Genet 68:320–326
Brown EG, Wood L, Wood S (1999) The medical dictionary for regulatory activities (MedDRA). Drug Saf 20:109–117
Bult CJ, Eppig JT, Kadin JA et al (2008) The Mouse Genome Database (MGD): mouse biology and model systems. Nucleic Acids Res 36(Database issue):D724–D728
Butte AJ (2008) Medicine. The ultimate model organism. Science 320:325–327
Butte AJ, Chen R (2006) Finding disease-related genomic experiments within an international repository: first steps in translational bioinformatics. AMIA Annu Symp Proc 2006:106–110
Butte AJ, Kohane IS (2006) Creation and implications of a phenome-genome network. Nat Biotechnol 24:55–62
Cantor MN, Sarkar IN, Bodenreider O et al (2005) Genestrace: phenomic knowledge discovery via structured terminology. Pac Symp Biocomput 2005:103–114
Chen DP, Weber SC, Constantinou PS et al (2008a) Novel integration of hospital electronic medical records and gene expression measurements to identify genetic markers of maturation. Pac Symp Biocomput 13:243–254
Chen ES, Hripcsak G, Xu H et al (2008b) Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 15:87–98
Dudley J, Butte AJ (2008) Enabling integrative genomic analysis of high-impact human diseases through text mining. Pac Symp Biocomput 13:580–591
Eppig JT, Blake JA, Bult CJ et al (2007) The mouse genome database (MGD): new features facilitating a model system. Nucleic Acids Res 35(Database issue):D630–D637
Fan JW, Friedman C (2008) Word sense disambiguation via semantic type classification. AMIA Annu Symp Proc 2008:177–181
Freimer N, Sabatti C (2003) The human phenome project. Nat Genet 34:15–21
Hamosh A, Scott AF, Amberger J et al (2000) Online Mendelian Inheritance in man (OMIM). Hum Mutat 15:57–61
Hernandez-Boussard T, Woon M, Klein TE et al (2006) Integrating large-scale genotype and phenotype data. OMICS Winter 10:545–554
Hristovski D, Peterlin B, Mitchell JA et al (2005) Using literature-based discovery to identify disease candidate genes. Int J Med Inform 74:289–298
Hunter PJ, Borg TK (2003) Integration from proteins to organs: the Physiome Project. Nat Rev Mol Cell Biol 4:237–243
Juristica I (2007) Integrative computatinal biology. In: Juristica I, Wigle DA, Wong B (eds) Cancer informatics in the post genomic era: towards information-based medicine. Springer, New York, pp 129–145
Kahraman A, Avramov A, Nashev LG et al (2005) PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics. Bioinformatics 21:418–420
Korbel JO, Doerks T, Jensen LJ et al (2005) Systematic association of genes to phenotypes by genome and literature mining. PLoS Biol 3:e134
Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921
Lenffer J, Nicholas FW, Castle K et al (2006) OMIA (Online Mendelian Inheritance in Animals): an enhanced platform and integration into the Entrez search interface at NCBI. Nucleic Acids Res 34(Database issue):D599–D601
Lindberg DA, Humphreys BL, McCray AT (1993) The Unified Medical Language System. Methods Inf Med 32:281–291
Lussier YA, Liu Y (2007) Computational approaches to phenotyping: high-throughput phenomics. Proc Am Thorac Soc 4:18–25
Lussier YA, Sarkar IN, Cantor M (2002) An integrative model for in-silico clinical-genomics discovery science. Proc AMIA Symp 2002:469–473
Mendonca EA, Cimino JJ, Johnson SB (2001) Using narrative reports to support a digital library. Proc AMIA Symp 2001:458–462
Miller R, Masarie FE, Myers JD (1986) Quick medical reference (QMR) for diagnostic assistance. MD Comput 3:34–48
Organization WH. International Classification of Diseases for Oncology, 3rd ed (ICD-O-3). Available from: http://www.who.int/classifications/icd/adaptations/oncology/en/
Pare G, Trudel MC (2007) Knowledge barriers to PACS adoption and implementation in hospitals. Int J Med Inform 76:22–33
Perez-Iratxeta C, Wjst M, Bork P et al (2005) G2D: a tool for mining genes associated with disease. BMC Genet 6:45
Physionet. http://www.physionet.org/.
Rebhan M, Chalifa-Caspi V, Prilusky J et al (1997) GeneCards: integrating information about genes, proteins and diseases. Trends Genet 13:163
Rebhan M, Chalifa-Caspi V, Prilusky J et al (1998) GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics 14:656–664
Rindflesch TC, Fiszman M (2003) The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform 36:462–477
Rindflesch TC, Tanabe L, Weinstein JN et al (2000) EDGAR: extraction of drugs, genes and relations from the biomedical literature. Pac Symp Biocomput 5:517–528
Safran M, Solomon I, Shmueli O et al (2002) GeneCards 2002: towards a complete, object-oriented, human gene compendium. Bioinformatics 18:1542–1543
Sam L, Liu Y, Li J et al (2007) Discovery of protein interaction networks shared by diseases. Pac Symp Biocomput 12:76–87
Sam LT, Mendonca EA, Li J et al (2009) PhenoGO: an integrated resource for the multiscale mining of clinical and biological data. Bmc Bioinformatics 10:S8
Shortliffe EH, Sondik EJ (2006) The public health informatics infrastructure: anticipating its role in cancer. Cancer Causes Control 17:861–869
Smith B, Ashburner M, Rosse C et al (2007) The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol 25:1251–1255
Spackman KA, Campbell KE, Cote RA (1997) SNOMED RT: a reference terminology for health care. Proc AMIA Annu Fall Symp 1997:640–644
Spasic I, Ananiadou S, McNaught J et al (2005) Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform 6:239–251
Stevens R, Goble CA, Bechhofer S (2000) Ontology-based knowledge representation for bioinformatics. Brief Bioinform 1:398–414
The Medical Subject Headings (MeSH) National Library of Medicine. http://www.nlm.nih.gov/mesh/.
Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351
Wang X, Friedman C, Chused A et al (2008) Automated knowledge acquisition from clinical narrative reports. AMIA Annu Symp Proc 6:783–787
Watson JD (1968) The double helix; a personal account of the discovery of the structure of DNA, 1st edn. Atheneum, New York
Wheeler DL, Church DM, Edgar R et al (2004) Database resources of the National Center for Biotechnology Information: update. Nucleic Acids Res 32(Database issue):D35–D40
Wong B (2007) Informatics. In: Juristica I, Wigle DA, Wong B (eds) Cancer informatics in the post genomic era: towards information-based medicine. Springer, New York, pp 87–145
Xu H, Fan JW, Hripcsak G et al (2007) Gene symbol disambiguation using knowledge-based profiles. Bioinformatics 23:1015–1022
Acknowledgements
This work was supported in part by the 1U54CA121852 (National Center for Multiscale Analyses of Genomic and Cellular Networks - MAGNET), the Cancer Research Foundation, the University of Chicago Cancer Research Center and the Ludwig Center for Metastasis Research.
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Mendonça, E.A., Lussier, Y.A. (2010). The Frontiers of Computational Phenomics in Cancer Research. In: Cho, W. (eds) An Omics Perspective on Cancer Research. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2675-0_11
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