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Computational Infrastructures for Large-Scale Data Access and Analysis in Post-Genomic Clinical Trials

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Systems Biology in Cancer Research and Drug Discovery

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Abstract

This Chapter reports on original results of the ACGT integrated project focusing on the design and development of a European Biomedical Grid infrastructure in support of multicentric, post genomic clinical trials on Cancer. ACGT is a FP6-IST research project developing open source middleware services layering to support multicentric, post-genomic Clinical Trials on Cancer. Post Genomic Clinical Trials use multilevel clinical and genomic data and advanced computational analysis and visualization tools to test hypotheses in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment. The ultimate goal of the ACGT is to supply a collection of open source services that will be re-used for building complex, discovery driven analytical workflows. This Chapter provides a detailed presentation of the needs of users involved in post-genomic clinical trials, and presents such needs in the form of scenarios which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented and its services are classified and discussed. A key set of such services are those used for wrapping heterogeneous clinical trial management systems and other public biological databases. In addition, the main technological challenge, i.e. the design and development of semantically rich Grid services is discussed. In achieving such an objective, extensive use of ontologies and metadata are required. The Master Ontology on Cancer, developed by the project, is presented and our approach to developing the required metadata registries, which provide semantically rich information about available data and computational services, is also provided. Finally, a discussion of the utilization of the infrastructure for the execution of highly complex computational work, that of modeling and simulation of tumor growth and response to treatment, is presented.

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Abbreviations

SNPs:

Single Nucleotide Polymorphisms

ACGT:

Advancing Clinico-Genomic Trials

TOP:

Test Of Principle

SIPO:

Serial-In to Parallel-Out

GPOH:

Gesellschaft fur Padiatrische Onkologie und Hamatologie

VO:

Virtual Organization

OGSA-DAI:

Open Grid Services Architecture-Data Access and Integration

PKI:

Public Key Infrastructure

NTP:

Network Time Server

CAS:

Central Authorization Service

ACGT MO:

The ACGT Master Ontology on Cancer

BPEL:

Business Process Execution Language

GSI:

Grid Security Infrastructure

RDF:

Resource Description Framework

OWL:

Web Ontology Language

GO HUGO:

Gene Ontology of Human Genome Organization

CaBIG:

Cancer Biomedical Informatics Grid

WSRF:

WS-Resource Framework

COG:

Children’s Oncology Group

SIOP:

International Society of Paediatric Oncology

TOP2A:

Topoisomerase IIA

IOS:

Integrated Oncosimulator

SPARQL:

SPARQL Protocol and RDF Query Language

References

  • Brochhausen M, Grigonyte G, Martín L, Graf N, Haller J, Smith B, Tsiknakis M (2011) The ACGT Master Ontology and its applications – Towards an ontology-driven cancer research and management system. J Biomed Inform 44(1):8–25

    Article  PubMed  Google Scholar 

  • Buetow KH (2005) Cyberinfrastructure: empowering a “Third Way” in biomedical research. Science Mag 308(5723):821–824

    CAS  Google Scholar 

  • Cannataro M, Comito C, Lo Schiavo F, Veltri P (2004) Proteus, a grid based problem solving environment for bioinformatics: architecture and experiments. IEEE Comput Intell Bull 3(1):7–18

    Google Scholar 

  • Chapin P, Skalka C, Wang X (2008) Authorization in trust management: features and foundations. ACM Comput Surv 40(3):48

    Article  Google Scholar 

  • Chen Y, Zhu J, Lum PY et al (2008) Variations in DNA elucidate molecular networks that cause disease. Nature 452:429–435

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • De Kraker J, Graf N, van Tinteren H, Pein F, Sandstedt B, Godzinski J, Tournade MF (2004) SIOP, Reduction of postoperative chemotherapy in children with stage I intermediate-risk and anaplastic Wilms’ tumour (SIOP 93–01 trial): a randomised controlled trial. Lancet 364:1229–1235

    Article  PubMed  Google Scholar 

  • Desmedt C, Di Leo A, de Azambuja E et al (2011) Multifactorial approach to predicting resistance to anthracyclines. J Clin Oncol 29(12):1578–1586. doi:10.1200/JCO.2010.31.2231

    Article  CAS  PubMed  Google Scholar 

  • Drmanac R, Sparks AB, Callow MJ et al (2010) Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327:78–81

    Article  CAS  PubMed  Google Scholar 

  • Dublin Core Metadata Standards. Available from: http://dublincore.org/ ;last visited: 01-28-2012

  • Farmer P, Bonnefoi H, Becette V et al (2005) Identification of molecular apocrine breast tumours by microarray analysis. Oncogene 24(29):4660–71

    Article  CAS  PubMed  Google Scholar 

  • Foekens JA, Atkins D, Zhang Y et al (2006) Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 10(24):1665–71

    Article  Google Scholar 

  • Graf N, Hoppe A, Georgiadi E, Belleman R, Desmedt C, Dionysiou D, Erdt M, Jacques J, Kolokotroni E, Lunzer A, Tsiknakis M, Stamatakos G (2009) The impact of ‘in silico’ oncology on clinical decision making in the context of nephroblastoma. Clin Pediatr (Klin Padiatr) 221:1–9

    Google Scholar 

  • Greenwood M, Goble C, Stevens R, Zhao J, Addis M, Marvin D, Moreau L, Oinn T (2003) Provenance of e-science experiments – experience from bioinformatics. In: Proceedings UK OST e-science 2nd All Hands Meeting (AHM’03), Nottingham, UK, pp 223–226

    Google Scholar 

  • Hu Z, Fan C, Oh DS et al (2006) The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 27(7):96

    Article  Google Scholar 

  • Jakoniene V, Lambrix P (2007) Ontology-based integration for bioinformatics. In: Collard M (ed) Ontologies-based (2006) databases and information systems, first and second VLDB workshops, ODBIS 2005/2006 Trondheim, Norway, 2–3 Sep 2005, pp 55–8

    Google Scholar 

  • Jemal A, Murray T, Samules A et al (2003) Cancer statistics. Cancer J Clin 53:5–26

    Article  Google Scholar 

  • Klein M (2001) Combining and relating ontologies: an analysis of problems and solutions. In: Gomez-Perez A, Gruninger M, Stuckenshmidt H, Uschold M (eds) Workshop on ontologies and information sharing, IJCAI’01, Seattle, WA

    Google Scholar 

  • Madnick S, Zhu H (2006) Improving data quality through effective use of data semantics. Data Knowl Eng 59(2):460–75

    Article  Google Scholar 

  • Mahalingam K, Huhns MN (1997) An ontology tool for query formulation in an agent-based context. In: Proceedings of the second IFCIS international conference on cooperative information systems, 24–27 June, Kiawah Island, SC. The IEEE Computer Society, Los Alamitos, CA, pp 170–8

    Google Scholar 

  • Multiscale Cancer Modeling (2011) Deisboeck TS and Stamatakos G (eds) CRC Press, Print ISBN: 978-1-4398-1440-6, eBook ISBN: 978-1-4398-1442-0

    Google Scholar 

  • Noy N, Musen M (2000) PROMPT: algorithm and tool for automated ontology merging and alignment. In: Proceedings of the seventeenth national conference on artificial intelligence (AAAI-2000); July 31–August 02, Austin, TX. The AAAI Press, Menlo Park, CA, pp 550–555

    Google Scholar 

  • OBO Relation Ontology. Available from: http://www.obofoundry.org/ro/ ;last visited: 01-28-2012

  • Perez-Rey D, Maojo V, Garcia-Remesal M et al (2006) ONTOFUSION: ontology-based integration of genomic and clinical databases. Comput Biol Med 36:712–30

    Article  CAS  PubMed  Google Scholar 

  • Rosse C, Mejino JLV (2004) A reference ontology for bioinformatics: the foundational model of anatomy. J Biomed Inform 36(6):478–500

    Article  Google Scholar 

  • Sciore E, Siegel M, Rosenthal A (1994) Using semantic values to facilitate interoperability among heterogeneous information systems. ACM Trans Database Syst 19(2):254–90

    Article  Google Scholar 

  • Sorlie T, Perou CM, Tibshirani R et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98(19):10869–74

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sotiriou C, Neo SY, McShane LM et al (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100(18):10393–8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stenzhorn H, Weiler G, Brochhausen M, Schera F, Kritsotakis V, Tsiknakis M, Kiefer S, Graf N (2010) The ObTiMA system – ontology-based managing of clinical trials, 13th International Congress on Medical Informatics (MEDINFO 2010), South Africa

    Google Scholar 

  • Tsiknakis M, Kafetzopoulos D, Potamias G et al (2006) Building a European biomedical grid on cancer: the ACGT Integrated Project. In: Proceedings of the HealthGrid 2006 conference, Stud Health Technol Inform 120:247–58

    Google Scholar 

  • Tsiknakis M, Brochhausen M, Nabrzyski J, Pucaski J, Sfakianakis S, Potamias G, Desmedt C, Kafetzopoulos D (2008) A semantic grid infrastructure enabling integrated access and analysis of multilevel biomedical data in support of post-genomic clinical trials on cancer. IEEE Trans Inf Technol Biomed 12(2):191–204, Special issue on Bio-Grids

    Article  Google Scholar 

  • Welch V (2004) Globus toolkit version 4 grid security infrastructure: a standards perspective. Available at: http://www.globus.org/toolkit/docs/4.0/security/GT4-GSI-Overview.pdf

  • Weng C, Gennari JH, Fridsma DB (2007) User-centered semantic harmonization: a case study. J Biomed Inform 40:353–64

    Article  PubMed  Google Scholar 

  • Yurkewicz K (2006) Accelerating Cancer Research. Science Grid. Available: http://www.interactions.org/sgtw/2006/0621/cabig_more.html

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Acknowledgment

The author acknowledges the highly constructive feedback provided by the external advisors appointed by the EC: D. Ingram, University College London, O. Björk, Karolinska University, Stockholm, L. Toldo, and E. Tsiporkova. The author also acknowledges the strong encouragement provided by the European Commission appointed project officer R. Bergström. Many thanks also go to the whole project implementation team for their inspiring and high quality work.

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Authors and Affiliations

  1. Computational Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece

    Manolis Tsiknakis

  2. Department of Applied Informatics and Multimedia, TEI, Heraklion, Crete, Greece

    Manolis Tsiknakis

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  1. Manolis Tsiknakis
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Correspondence to Manolis Tsiknakis .

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Editors and Affiliations

  1. Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA

    Asfar S. Azmi

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Tsiknakis, M. (2012). Computational Infrastructures for Large-Scale Data Access and Analysis in Post-Genomic Clinical Trials. In: Azmi, A.S. (eds) Systems Biology in Cancer Research and Drug Discovery. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4819-4_16

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