Predictive gene signatures and transcriptional regulatory networks of spontaneous regression in neuroblastoma

Coordinator:		PD Dr. Matthias Fischer
Institution:		Klinik und Poliklinik für Allgemeine Kinderheilkunde der Universität zu Köln Abteilung Kinderonkologie und -hämatologie
Homepage:		cms.uk-koeln.de/kinderonkologie

Neuroblastoma is a tumor arising from cells of the developing sympathetic nervous system. It is one of the most frequent solid tumors in childhood with about 130 newly diagnosed cases in Germany per year. The natural courses of neuroblastoma are remarkably heterogeneous: Relentless progression occurs in about half of the affected children, which frequently results in death of disease despite the most intensive multimodal therapies. In contrast, spontaneous regression of the disease without any cytotoxic treatment is observed in other children. The molecular basis of the different natural courses is still not known. In addition, discrimination of the divergent subtypes at the time of diagnosis has remained a challenge to physicians. However, an accurate prediction of the course of the disease is an indispensable prerequisite for the selection of the most adequate treatment of each individual patient.
The goal of our project within the NGFN-Plus is the comprehensive molecular characterization of the various neuroblastoma subtypes to improve risk estimation of neuroblastoma patients and to elucidate the mechanisms of disease development. For this purpose, we are using gene expression microarrays that are able to simultaneously determine the mRNA levels of thousands of genes. In collaboration with researches of the German Cancer Research Center, we have previously defined a prognostic gene expression signature that predicts patients’ outcome more accurately than current prognostic markers (fig. 1 and 2). In NGFN-Plus, we are evaluating these results in a larger cohort of patients, and we are aiming to test the classifier in clinical practice. Moreover, we are utilizing our comprehensive gene expression database to identify genes that may be important for the development of the distinct neuroblastoma subtypes. To evaluate the functional relevance of the selected genes, genetically modified neuroblastoma cell lines are generated, in which the consequences of up-regulation or down-regulation of the respective genes can be investigated. A better understanding of the molecular biology of neuroblastoma may enable the development of novel therapeutic strategies that specificly target relevant pathways of the tumor and may improve the long-term prognosis of affected children in the future.





                                            
Fig. 1:
Hierarchical cluster analysis of 174 neuroblastomas using gene expression data of the 144 classifier genes. Lines represent tumors, columns represent genes. Gene expression levels are visualized as log-values ranging from blue (+1.0) to red (-1.0). The column on the right hand side indicates the results of the classifier prediction (green, favorable; red, unfavorable).

Fig. 2:
Kaplan-Meier estimates for event-free survival for 171 patients that were subdivided into (A) low-, (B) intermediate-, and (C) high-risk groups according to the NB2004 stratification system after classification by the 144-gene classifier (F, favorable classifier prediction; UF, unfavorable classifier prediction).