CIT Program Tumor Identity Cards: Difference between revisions

Revision as of 14:07, 21 February 2014

Revision as of 22:37, 25 March 2014

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== Collaborations throughout the whole of France ==

Built with a network of researchers, [[pathology|pathologists]], doctors and [[bioinformatician]]s, 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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The quality of the biological resources constitutes a major element in the [[reproducibility]] of the results obtained in the [[Genomics|genomic]] studies.

To this end, the CIT program has set up a platform dedicated to the extraction and qualification of [[RNA]] and [[DNA]]. The extraction of RNA and DNA is carried out from the same tumor sample. The quality of the samples is assessed using [[Agarose gel electrophoresis|agarose gels]] and electrophoregram profiles in order to evaluate the level of contamination and [[biodegradation|degradation]] of these biological resources.

All the samples are processed on the same platform, with validated and standardized protocols, to optimize the yield of the platform, to implement precise [[quality control]]s, and to improve the performance of the subsequent [[hybridizationNucleic acid hybridisation|hybridizations]]s even in case of partial degradation of the tumor samples. Depending on the quality of the surgical procedure, the volume of available [[biomaterial]] and the type of pathology, between 0 and 60% (average 25%) of the sample RNAs are filtered out by the platform.

=== Biochip Experiments ===

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==== Data analysis ====

The data analysis unfolds into three main stages:

* Class discovery, using unsupervised [[cluster analysis|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.