Research in our group aims at a comprehensive characterization of the neuroblastoma trancriptome to precisely define molecular subtypes of this tumor, to evaluate gene expression based prognostic markers and to identify novel genes associated with specific phenotypes of neuroblastoma.
Construction and evaluation of a custom-made neuroblastoma oligonucleotide-microarray (“neuroblastoma chip”)
Clustering analysis of the "neuroblastoma chip"
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A wide range of diagnostic procedures is available to characterize NB. The prognostic predictive value is, however, uncertain, as shown by the heterogeneous treatment outcome even within the same defined risk groups. To develop a robust risk stratification tool based on gene expression data, all key genetic determinants dictating NB phenotypes should be adequately represented on such a diagnostic tool. We have gathered gene expression data from a large set of NB tumors using different high-throughput gene expression analysis tools: standard expression arrays, customized arrays based on subtractive cDNA libraries and SAGE libraries were used to define a comprehensive list of genes reflecting the expression repertoire of individual NB phenotypes. In addition, transcripts mapping to frequently altered chromosomal regions were included. Based on this unique compilation of NB phenotype-specific transcripts, we designed a customized microarray consisting of 10.163 oligos representing 8.155 Unigene clusters and more than 2.000 newly designed probes for transcripts that were not covered by current “whole-genome” arrays.
To evaluate the prognostic value of the neuroblastoma chip, 502 expression profiles were generated retrospectively from 251 primary tumors. For identification of a predictive gene signature, a PAM algorithm was applied to a first set of 77 tumors of patients with maximally divergent clinical outcome. The predictive power of the resulting 144 gene PAM classifier was evaluated in a second set of 174 patients. We could demonstrate that classification errors made by the currently used classification systems would have been corrected by a gene expression based classification system. Improvement of prediction accuracy was observed in all NB risk groups. Thus, NB patients may largely benefit from a gene expression based classification system as therapeutic intensity ranging from a wait-and-see approach to multimodality therapy can be tailored to the individual risk of the patient. To further evaluate our gene expression based risk stratification tool, all newly diagnosed patients enrolled in the German Neuroblastoma Trial 2004 are prospectively analyzed. To our knowledge, this is the first time that a gene expression based classification system is incorporated into a nation-wide clinical cancer trial.
Microarray-analysis identified gene signatures of progressing versus regressing NB
To understand the divergent biological behavior of NB and to translate this knowledge for patients benefit, there is need to address the complexity of cellular signaling coordinates in tumor progression, spontaneous regression and therapy resistance. As a point of departure, we have used microarray technology to identify genetic determinants of progressing versus regressing neuroblastomas. We generated cDNA microarray profiles from 53 NB and demonstrated that the global patterns of gene expression largely reflect the clinical phenotype of the tumors independent of currently used risk factors including amplified MYCN [1]. In order to identify biological processes and signaling pathways that may contribute to an increasingly aggressive phenotype, we used two approaches: (1) a principle component analysis (PCA)-based approach to data analysis allowing to associate gene expression profiles with gene ontology (GO) annotations [2], (2) a combined analysis of gene expression data from tumor samples and cell culture models with targeted activation of MYCN or E2F1, two major determinants of NB development [3]. Both approaches identified a specific subset of cell cycle and/or chromosome segregation genes that are deregulated in aggressive NB. We then generated NB cell lines with targeted expression of identified candidate genes (cDNAs or siRNA-like transcripts) and demonstrated that MYCN activates this genetic program indirectly via disrupting specific components of the Rb pathway (Rb-Skp2) leading to deregulated E2F activity. In contrast to aggressive NB, regressing NB fail to activate these specific genetic programs, indicating that components controlling Rb-Skp2 are still functional in these tumors [3]. We will now systematically explore whether differentially expressed genes within progressing and regressing tumors have Rb-Skp2 regulating activity.