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Integrating Clinical and Genomic Information through the PrognoChip Mediator

Integrating Clinical and Genomic Information through the PrognoChip Mediator. Implemented by Manos Kalaitzakis, Dimitris Manakanatas, Haridimos Kondylakis, Manolis Nikoloudakis , and Maria Michou Under the guidance of Anastasia Analyti & Dimitris Plexousakis

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Integrating Clinical and Genomic Information through the PrognoChip Mediator

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  1. Integrating Clinical and Genomic Information through the PrognoChip Mediator Implemented by Manos Kalaitzakis, Dimitris Manakanatas, Haridimos Kondylakis, Manolis Nikoloudakis, and Maria Michou Under the guidance of Anastasia Analyti & Dimitris Plexousakis Institute of Computer Science, FORTH-ICS, Greece Department of Computer Science, University of Crete, Greece Medical Doctors Elias Sanidas, Efstathios Stathopoulos, Maria Kafousi Medical School, University of Crete, Greece Stamatis Vassilaros Prolipsis Diagnostic Breast Center, Athens, Greece

  2. The aim of PrognoChip is: • the identification of molecular markers for the classification and prognosis of Breast Cancer, based on: the correlation of patient’s clinicohistopathological parameters and therapy response with their tumor gene expression profiles

  3. Sample 1 Sample 2 paraffin RNA later Paraffin cuts Tissue Bank coloring extraction Microscope Microarray experiment biopsis FORTH-IMBB: Molecular Biology Lab • report Tumor Tissue Route liquid nitrozen or ice Surgery Tumor sample Histopathologist PAGNH: University Hospital PROLIPSIS: Breast Cancer Center

  4. The Integrated Clinico-Genomics Environment of PrognoChip Consists of: • aClinical Information System to keep patient clinical information (i.e., clinical, laboratory, and histopathological information), • a Genomic Information System to manage DNA microarray experiments, analyze raw hybridization data, and store gene expression profiles, • a middleware layer, called PrognoChip Mediator, for the integration of the Clinical and Genomic Information Systems, and • a Data Mining layer, for the intelligent processing of the retrieved clinico-genomic data, knowledge extraction, and visualization.

  5. Three Databases to Integrate • Genomic Information System (extension of BASE v1.2.16, developed by Lund Univ.) • microarray design, samples, hybridizations, etc. • Clinical Information System (extension of Integrated Care Solutions, developed by FORTH-ICS.) • patient medical history, current findings, diagnostic test results, tumor histopathologic diagnosis, etc. • Gene Ontology (GO) Database • ontology terms for the annotation of gene products

  6. medical doctor Submit/search/ update Visualization Tools findings PrognoChip: The Integrated Clinico-Genomics Environment metadata Findings Repository Data Mining Tools store/query findings Specify parameters output XML file biomedical investigator (local access) Mediator DB Mediator store administration & temporary data virtual query Query Engine (web-based) biologist Clinical IS Submit/search/ update Genomic IS GO database (client-server based) (web-based) local installation Normalization Plug-ins

  7. The PrognoChip Mediator • The authorized biomedical investigator can form clinico-genomic queries through a web-based graphical user interface. • Specifically, the user can specify criteria for selecting tumors: • excised from patients with a clinical profile of interest, • having histopathologic characteristics of interest, and • participating in a PrognoChip microarray experiment of specified quality and characteristics. • Returned tumors are accompanied with desirable clinico-genomic information (e.g., gene expression profiles).

  8. Clinical Criteria • Patient past breast diseases • Gynecological history (e.g. age of menarche, menopausal status, etc.) • Total duration of hormone intake • Other risk factors (e.g. smoking habits, etc. ) • Family history of malignancy • Current findings and blood tumor markers • Therapies before and after surgery • Effectiveness of therapies

  9. Clinical Query Forms

  10. 10 11

  11. Tumor Histopathologic Criteria • Tumor histological type • Diameter • Grade • Lymphatic, Veinous, Perineural, Skin, Nipple, and Lymph node invasion • Histological TMN • Immunohistochemical tumor markers (ER, HER-2, etc.)

  12. Histopathologic Query Forms

  13. Genomic Criteria • The channel of the cancerous tissue • Hybridization quality indicators • Dates of hybridizations • The name of the desired normalization procedure • Reporter/Gene filtering criteria • GO annotations and evidence code • Molecular Function, Biological Process, and Cellular Location ontology terms

  14. Genomic Query Forms

  15. Select Page After patient/tumor criteria specification, the user selects the clinical,histopathologic, and genomic fields of interest that will accompany the cancerous sample in the output.

  16. Select Page: Part 1

  17. Select Page: Part 2

  18. Query Processing • When a Query Form is saved, part of the related subquery is formed. • When the Select Page is filled out, final clinical subqueries are formed. • Clinical subqueries are submitted to Clinical IS and results are composed. • Based on the tumor names returned in 3), final genomic queries are formed and submitted to Genomic IS. • Results from Clinical IS and Genomic IS are joined based on tumor name. • OutputXML file is formed, based on a predetermined schema.

  19. Output XML File Schema • For the full Output XML File schema, see: • http://www.ics.forth.gr/~analyti/PrognoChip/OutputXMLFileSchema.jpg • Description is provided in: • http://www.ics.forth.gr/~analyti/PrognoChip/OutputXMLFileDescription.pdf

  20. Result Page • In the Result Page, the user can: • download the complete set of output files to his/her machine, • view the output XML file, and • download previous results.

  21. Output XML File

  22. Data Mining Layer • It supports decision making operations, on output XML files. • Examples: • Retrieve the tumor gene expression profiles of patients that meet clinical profile A and clinical profile B. Then, find the genes that best discriminate between these two groups. • Cluster patients based on their tumor gene expression profiles. Then, identify indicative patient clinical profiles, for these clusters.

  23. Details on the PrognoChip Mediator can be found in: • Anastasia Analyti, Haridimos Kondylakis, Dimitris Manakanatas, Manos Kalaitzakis, Dimitris Plexousakis, George Potamias, Integrating Clinical and Genomic Information through the PrognoChip Mediator, Procs. of the 7th International Symposium on Biological and Medical Data Analysis (ISBMDA-2006), 250-261, 2006. Springer-Verlag

  24. PrognoChip Mediator Web Site • http://www.ics.forth.gr/isl/projects/PrognoChip/

  25. PrognoChip 2004-2007 Acknowledgements The development of PrognoChip Mediator was supported by National funds within the context of: • the PrognoChip project (GSRT-EPAN).

  26. Thank you !!!

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