PROGNOCHIP
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The “Prognochip” project, initiated in 2004 and funded
by the Greek General Secretariat for Research and Technology, 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.
Breast cancer 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 such as lymph node status, estrogen receptor (ER) status
or histological grade 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. This groundbreaking approach for cancer classification
and diagnosis promises to provide with accurate prognosis a clear benefit
to almost three out of four women who receive aggressive chemotherapy
treatment, although they would have survived without it.
The major tasks within the Prognochip project are as follows: Patients
are informed and consent to the molecular and genetic data analysis
of their tumour specimens and blood samples, provided that their anonymity
is ensured. A tissue procurement protocol has been designed for tissue
collection and storage and a tissue-bank system has been established
for proper tissue filing and management. An RNA integrity assay has
been developed for ensuring the quality of clinical samples included
in the study. Patients with malignant tumours are staged according to
the TNM system and a set of immunohistological markers are examined.
A DNA microarray of long oligonucletide probes has been designed, representing
all known human genes – approximately 35,000 different transcripts
of 27,000 different genes. A common reference material has been designed
for the study, consisting of a defined set of cell-line extracts, thereby
ensuring accurate quantitation of gene expression. After hybridization,
fluorescence intensity images representing gene expression levels are
stored in BASE, a comprehensive MySQL database server that manages massive
amounts of data generated by microarray analysis, biomaterial information
and raw data. Special plug-ins have been created for data pre-processing
(filtering, normalization) and analysis.
In general, two computational approaches from a suite of intelligent
data processing tools are used for tumour classification. The first
approach 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.
The 'supervising' parameters can be clinical features such as the clinical
outcome (including overall survival, relapse-free survival times, metastasis
etc), other molecular markers, chromosomal aberrations, patterns observed
with other diagnostic methods and responses to (chemo)therapy. In addressing
classification, there are two issues: a) class discovery, the definition
of previously unrecognized tissue sub-types, and b) class prediction,
the assignment of particular samples to existing classes (this could
reflect current states or even future outcomes).
Related Publications:
1st International Advanced Research Workshop on In Silico Oncology
2nd International Conference on Information Communication Technologies in Health
17ème Congrès Mondial IMACS Calcul Scientifique, Mathématiques Appliquées et Simulation
Annual Meetings
Protocols
RNA Stabilization with RNAlater RNA Stabilization Reagent
RNA Amplification and Labelling
Hybridization of DNA Microarray Slides
