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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

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

slide2
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

tumor tissue route

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

the integrated clinico genomics environment of prognochip
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.
three databases to integrate
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
slide6

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

the prognochip mediator
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).
clinical criteria
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
slide11

10

11

tumor histopathologic criteria
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.)

genomic criteria
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
slide30

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.

query processing
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.
output xml file schema
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
slide37

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.
data mining layer
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.
slide41
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
prognochip mediator web site
PrognoChip Mediator Web Site
  • http://www.ics.forth.gr/isl/projects/PrognoChip/
acknowledgements

PrognoChip

2004-2007

Acknowledgements

The development of PrognoChip Mediator was supported by National funds within the context of:

  • the PrognoChip project (GSRT-EPAN).