The concept of translational research is always hampered by the problem that most of the disease phenotypes do not have a mono causal origin. Therefore most treatment schemes based on one to three drugs are not really productive for most of the patients even if the patients are carefully selected from the responder group. Here the array techniques has inspired many research groups to develop algorithms deriving interaction networks or regulatory networks from this type of data to better get rid of the complexity of the biochemical interactions. The challenge is to find networks and to select the group of master nodes which might be good targets for a balanced multi-drug treatment. This means not only to measure one data type with array techniques but to join array data from multiple platforms and different data levels. Our goal is to integrate these data types to form networks with a predictive character for osteosarcomas. The existing web platform CAPweb/VAMP from the Institute Curie is based on a Java web-client and R. This platform is focused on array data analysis and visualisation, can be extended by additional R modules and is therefore an excellent choice to implement further algorithms for data integration and network prediction. We are now establishing algorithms beyond a pure association of effects like permutation procedures for optimal rank orders of effects in a given subset of 16 factors which can be assembled to bigger units and selection procedures of gene expression signals by gene dosage concepts. The presented approach is sustainable because the platform can be constantly extended and improved. On the other hand this platform is end-user suitable. This is the best way to bring theoretical concepts to the bench scientist. As a consequence translational research will become more real and complex systems more feasible.
Osteosarcoma (OS) is the most common primary malignancy of bone, with up to 80% of patients suffering from metastatic or micrometastatic disease at the time of diagnosis. For the metastatic potential of tumours invasiveness plays an important role. This study intends to determine new candidate genes for cell invasiveness. Eight OS cell lines (MNNG, HOS, MG63, SJSA1, OST, ZK58, U2OS, SAOS) were analysed using a modified Boyden Chamber Assay to separate invasive and non-invasive cells. Total RNA isolation and Illumina hybridisation Arrays (V3 bead arrays) were performed for both fractions. Out of the eight cell lines, five (MNNG, HOS, MG63, SJSA1, OST) displayed an invasive fraction between 1.76 and 0.02%, which proved sufficient for subsequent RNA analysis. Pair wise comparison yielded 161 differently expressed genes between invasive and non-invasive cells. These are involved in important pathways such as cell motility, cell communication or signal transduction. The generated new candidate genes might play an important role in metastasis of OS. Their functional characterization has been started combining knockdown experiments (RNAi) with the invasion assay. Validation will be done by RT-PCR and immunohisto-chemistry on a larger sample using OS-TMAs. Determined genes and pathways will be correlated with clinical parameters like metastasis, survival and chemotherapy sensitivity in order to improve understanding of the biology of OS.
