NGFN-PLUS
NGFN Leukemia Bioinformatics Resource
| Coordinator: | Dr. Claudio Lottaz | |
| Institution: | Institut für Funktionelle Genomik, Universität Regensburg | |
| Homepage: | www-compdiag.uni-regensburg.de/research/ |
Our focus is on studying gene expression profiles from patients and model systems, and to integrate data within and across sub-projects of the consortium. While transcriptomic (gene expression) data makes up the backbone of our studies, genomic data (changes in the sequence and copy number of the DNA) as well as clinical data (e.g. blood cell counts) provide valuable additional insight into the molecular causes of leukemia.
Some genes turn into oncogenes when they are expressed at non-normal levels and help cancer cells to avoid cell death (apoptosis). These oncogenes activate pathways in cancers which would otherwise be inactive. Within the consortium, controlled experiments with deregulation of one specific gene within a pathway will be performed to derive molecular signatures. We will search for these pathway signatures in profiles of cancer patients and study their pathway activity. Knowing which pathways are active in an individual patient gives a more fine-grained diagnosis.
Besides integrating data generated within the consortium, we will make use of public data and integrate it with data of the consortium. The Connectivity Map (Lamb, 2006) is a database consisting of gene expression signatures from different cell lines exposed to more than 1000 chemical compounds. We developed a robust approach to compare these signatures to signatures of leukemia patients. This allows us to relate the effects of chemical compounds in the database to gene expression changes observed in subgroups of leukemia patients. With this approach we search for compounds and combinations of compounds to improve treatment. For example, a compound which reverses the signature of an aggressive tumor to a less aggressive one could serve as a therapy adjuvant.
Figure legend:
Connections between compounds of the Connectivity Map and leukemia datasets. A green edge indicates direct similarity, red reversed similarity, blue similarity in both directions, black indicates that results from replicate experiments were contradictory. Some compounds connect to many datasets indicating similar changes in gene expression between compound treatment and the leukemia datasets.
Further Coordinators:
Some genes turn into oncogenes when they are expressed at non-normal levels and help cancer cells to avoid cell death (apoptosis). These oncogenes activate pathways in cancers which would otherwise be inactive. Within the consortium, controlled experiments with deregulation of one specific gene within a pathway will be performed to derive molecular signatures. We will search for these pathway signatures in profiles of cancer patients and study their pathway activity. Knowing which pathways are active in an individual patient gives a more fine-grained diagnosis.
Besides integrating data generated within the consortium, we will make use of public data and integrate it with data of the consortium. The Connectivity Map (Lamb, 2006) is a database consisting of gene expression signatures from different cell lines exposed to more than 1000 chemical compounds. We developed a robust approach to compare these signatures to signatures of leukemia patients. This allows us to relate the effects of chemical compounds in the database to gene expression changes observed in subgroups of leukemia patients. With this approach we search for compounds and combinations of compounds to improve treatment. For example, a compound which reverses the signature of an aggressive tumor to a less aggressive one could serve as a therapy adjuvant.
Figure legend:
Connections between compounds of the Connectivity Map and leukemia datasets. A green edge indicates direct similarity, red reversed similarity, blue similarity in both directions, black indicates that results from replicate experiments were contradictory. Some compounds connect to many datasets indicating similar changes in gene expression between compound treatment and the leukemia datasets.
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