Computational BioMedicine Laboratory
Project Title: PROGNOCHIP
Funding Organization: GSRT
Partners: PROLIPSIS S.A, UNIVERCITY OF CRETE-MEDICAL SCHOOL, WORLD HEALTH ORGANIZATION-INTERNATIONAL AGENCY FOR RESEARCH ON CANCER
Expiration Date: 2006
Total Budget: 587.000 Euro
FORTH ICS budget: 52.000 Euro
Project Objective: The “Prognochip” project, initiated in 2004 is funded by the Greek General Secretariat for Research and Technology. It brings together scientists with different expertise from distant scientific disciplines, such as medicine, molecular biology, bioinformatics, medical informatics and biostatistics, who join forces and efforts to identify and validate “signature” gene expression profiles of breast tumours that correlate with other epidemiological or clinical parameters. Prognochip, focuses on breast cancer which is one of the most common malignancies affecting women, the life time risk being approximately 10%. Breast cancer is both genetically and histopathologically heterogeneous, and the mechanisms underling its development remains largely unknown. Although, conventional prognostic indicators do exist, such as lymph node status, estrogen receptor (ER) status or histological grade, and are extremely valuable, it is still particularly difficult to predict which patients will develop metastases. Global gene expression analysis using microarrays offers unprecedented opportunities to correlate tumour molecular signatures with the clinical outcome of the disease and achieve cancer classification. From the computational point of view, two approaches from a suite of intelligent data processing tools are used for tumour classification. The first is the “unsupervised” analysis, in which no source of knowledge is used to guide the analysis process. Instead, the data are searched for patterns with no a priori expectation concerning the number or type of groups (gene and tumour clusters) that might be present. The second is the “supervised” analysis, in which we search for genes whose expression patterns correlate with external parameters. These Knowledge discovery approaches as well the fundamental challenge of integration of heterogeneous, distributed databases managing multilane clinical and genomic data present the main challenges of the project.