CIT Program Tumor Identity Cards: Difference between revisions

Revision as of 20:02, 2 October 2010

Revision as of 08:03, 15 October 2010

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[[Image:CIT-Tumor-Samples-20100507.png|thumb|upright=2.5|The CIT Program database contains more than 8,000 cancer samples]]

The '''"Cartes d'Identité des Tumeurs (CIT)" program''' (or 'Tumor Identity Cards'), launched and financed by the French charity "Ligue Nationale contre le Cancer", aims at characterizing multiple types of tumors through the coupled genomic '''analysis of gene expression and chromosomal alterations'''.

== Towards personalized treatments ==

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The "Cartes d'Identité des Tumeurs (CIT)" program should benefit each patient by contributing to :

* more accurate [[Medical diagnosis|diagnosis]]

* better predictions of the response to [[Therapy|treatment]] and the disease progression

* improved patient follow-up during and after treatment

== Collaborations throughout the whole of France ==

Built with a network of researchers, pathologists, doctors and bioinformaticians, the "Cartes d'Identité des Tumeurs (CIT)" program involves 60 teams and offers one of the largest tumor databases in Europe containing more than 8,000 annotated tumor samples and 10,000 micro-array experiments.

== A set of standardized processes and technologies ==

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=== Analysis & Validation ===

CIT Biostatistical expertise

The data processing procedures are based on reference methods from the literature or on innovative internal developments. Implemented with the open source statistical software R, they follow a set of specifications which facilitate collaborative work and tracking.

===== Pre-processing =====

The data sent by the hybridization platforms are pre-processed according to a normalization and quality control stage adapted to each technology: background correction, quality control, filtering, aggregation and normalization. For genomic data (CGH, SNPs), an essential segmentation step is added to identify the altered regions along the genome.

===== Data analysis =====

The data analysis unfolds into three main stages:

* Class discovery, using unsupervised clustering, enables the identification of the underlying molecular groups. The quality and variety of the supplied annotations are crucial to interpret the resulting classification.

* Class comparison, through a supervised approach, defines a molecular signature, i.e. the set of markers associated with a given phenotype.

* Class prediction, using classification approaches, establishes the smallest combinations of molecular markers to characterize tumor groups and to guide decisions about medical treatments.