Joëlle Vermeulen

Overall Genomic Pattern Is a Predictor of Outcome in Neuroblastoma

PURPOSE For a comprehensive overview of the genetic alterations of neuroblastoma, their association and clinical significance, we conducted a whole-genome DNA copy number analysis. PATIENTS AND METHODS A series of 493 neuroblastoma (NB) samples was investigated by array-based comparative genomic hybridization in two consecutive steps (224, then 269 patients). RESULTS Genomic analysis identified several types of profiles. Tumors presenting exclusively whole-chromosome copy number variations were associated with excellent survival. No disease-related death was observed in this group. In contrast, tumors with any type of segmental chromosome alterations characterized patients with a high risk of relapse. Patients with both numerical and segmental abnormalities clearly shared the higher risk of relapse of segmental-only patients. In a multivariate analysis, taking into account the genomic profile, but also previously described individual genetic and clinical markers with prognostic significance, the presence of segmental alterations with (HR, 7.3; 95% CI, 3.7 to 14.5; P < .001) or without MYCN amplification (HR, 4.5; 95% CI, 2.4 to 8.4; P < .001) was the strongest predictor of relapse; the other significant variables were age older than 18 months (HR, 1.8; 95% CI, 1.2 to 2.8; P = .004) and stage 4 (HR, 1.8; 95% CI, 1.2 to 2.7; P = .005). Finally, within tumors showing segmental alterations, stage 4, age, MYCN amplification, 1p and 11q deletions, and 1q gain were independent predictors of decreased overall survival. CONCLUSION The analysis of the overall genomic pattern, which probably unravels particular genomic instability mechanisms rather than the analysis of individual markers, is essential to predict relapse in NB patients. It adds critical prognostic information to conventional markers and should be included in future treatment stratification.

Meta-analysis of Neuroblastomas Reveals a Skewed ALK Mutation Spectrum in Tumors with MYCN Amplification

Purpose: Activating mutations of the anaplastic lymphoma kinase (ALK) were recently described in neuroblastoma. We carried out a meta-analysis of 709 neuroblastoma tumors to determine their frequency and mutation spectrum in relation to genomic and clinical parameters, and studied the prognostic significance of ALK copy number and expression. Experimental Design: The frequency and type of ALK mutations, copy number gain, and expression were analyzed in a new series of 254 neuroblastoma tumors. Data from 455 published cases were used for further in-depth analysis. Results: ALK mutations were present in 6.9% of 709 investigated tumors, and mutations were found in similar frequencies in favorable [International Neuroblastoma Staging System (INSS) 1, 2, and 4S; 5.7%] and unfavorable (INSS 3 and 4; 7.5%) neuroblastomas (P = 0.087). Two hotspot mutations, at positions R1275 and F1174, were observed (49% and 34.7% of the mutated cases, respectively). Interestingly, the F1174 mutations occurred in a high proportion of MYCN-amplified cases (P = 0.001), and this combined occurrence was associated with a particular poor outcome, suggesting a positive cooperative effect between both aberrations. Furthermore, the F1174L mutant was characterized by a higher degree of autophosphorylation and a more potent transforming capacity as compared with the R1275Q mutant. Chromosome 2p gains, including the ALK locus (91.8%), were associated with a significantly increased ALK expression, which was also correlated with poor survival. Conclusions: ALK mutations occur in equal frequencies across all genomic subtypes, but F1174L mutants are observed in a higher frequency of MYCN-amplified tumors and show increased transforming capacity as compared with the R1275Q mutants. Clin Cancer Res; 16(17); 4353–62. ©2010 AACR.

Small-molecule MDM2 antagonists as a new therapy concept for neuroblastoma.

Circumvention of the p53 tumor suppressor barrier in neuroblastoma is rarely caused by TP53 mutation but might arise from inappropriately increased activity of its principal negative regulator MDM2. We show here that targeted disruption of the p53-MDM2 interaction by the small-molecule MDM2 antagonist nutlin-3 stabilizes p53 and selectively activates the p53 pathway in neuroblastoma cells with wild-type p53, resulting in a pronounced antiproliferative and cytotoxic effect through induction of G(1) cell cycle arrest and apoptosis. A nutlin-3 response was observed regardless of MYCN amplification status. Remarkably, surviving SK-N-SH cells adopted a senescence-like phenotype, whereas CLB-GA and NGP cells underwent neuronal differentiation. p53 dependence of these alternative outcomes of nutlin-3 treatment was evidenced by abrogation of the effects when p53 was knocked down by lentiviral-mediated short hairpin RNA interference. The diversity of cellular responses reveals pleiotropic mechanisms of nutlins to disable neuroblastoma cells and exemplifies the feasibility of exploiting, by a single targeted intervention, the multiplicity of anticancer activities exerted by a key tumor suppressor as p53. The observed treatment effects without the need of imposing a genotoxic burden suggest that selective MDM2 antagonists might be beneficial for treatment of neuroblastoma patients with and without MYCN amplification.

MYCN/c-MYC-induced microRNAs repress coding gene networks associated with poor outcome in MYCN/c-MYC-activated tumors

Increased activity of MYC protein-family members is a common feature in many cancers. Using neuroblastoma as a tumor model, we established a microRNA (miRNA) signature for activated MYCN/c-MYC signaling in two independent primary neuroblastoma tumor cohorts and provide evidence that c-MYC and MYCN have overlapping functions. On the basis of an integrated approach including miRNA and messenger RNA (mRNA) gene expression data we show that miRNA activation contributes to widespread mRNA repression, both in c-MYC- and MYCN-activated tumors. c-MYC/MYCN-induced miRNA activation was shown to be dependent on c-MYC/MYCN promoter binding as evidenced by chromatin immunoprecipitation. Finally, we show that pathways, repressed through c-MYC/MYCN miRNA activation, are highly correlated to tumor aggressiveness and are conserved across different tumor entities suggesting that c-MYC/MYCN activate a core set of miRNAs for cooperative repression of common transcriptional programs related to disease aggressiveness. Our results uncover a widespread correlation between miRNA activation and c-MYC/MYCN-mediated coding gene expression modulation and further substantiate the overlapping functions of c-MYC and MYCN in the process of tumorigenesis.

Measurable impact of RNA quality on gene expression results from quantitative PCR

Compromised RNA quality is suggested to lead to unreliable results in gene expression studies. Therefore, assessment of RNA integrity and purity is deemed essential prior to including samples in the analytical pipeline. This may be of particular importance when diagnostic, prognostic or therapeutic conclusions depend on such analyses. In this study, the comparative value of six RNA quality parameters was determined using a large panel of 740 primary tumour samples for which real-time quantitative PCR gene expression results were available. The tested parameters comprise of microfluidic capillary electrophoresis based 18S/28S rRNA ratio and RNA Quality Index value, HPRT1 5′–3′ difference in quantification cycle (Cq) and HPRT1 3′ Cq value based on a 5′/3′ ratio mRNA integrity assay, the Cq value of expressed Alu repeat sequences and a normalization factor based on the mean expression level of four reference genes. Upon establishment of an innovative analytical framework to assess impact of RNA quality, we observed a measurable impact of RNA quality on the variation of the reference genes, on the significance of differential expression of prognostic marker genes between two cancer patient risk groups, and on risk classification performance using a multigene signature. This study forms the basis for further rational assessment of reverse transcription quantitative PCR based results in relation to RNA quality.

An integrative genomics screen uncovers ncRNA T-UCR functions in neuroblastoma tumours

Different classes of non-coding RNAs, including microRNAs, have recently been implicated in the process of tumourigenesis. In this study, we examined the expression and putative functions of a novel class of non-coding RNAs known as transcribed ultraconserved regions (T-UCRs) in neuroblastoma. Genome-wide expression profiling revealed correlations between specific T-UCR expression levels and important clinicogenetic parameters such as MYCN amplification status. A functional genomics approach based on the integration of multi-level transcriptome data was adapted to gain insights into T-UCR functions. Assignments of T-UCRs to cellular processes such as TP53 response, differentiation and proliferation were verified using various cellular model systems. For the first time, our results define a T-UCR expression landscape in neuroblastoma and suggest widespread T-UCR involvement in diverse cellular processes that are deregulated in the process of tumourigenesis.

Prognostic Impact of Gene Expression–Based Classification for Neuroblastoma

PURPOSE To evaluate the impact of a predefined gene expression-based classifier for clinical risk estimation and cytotoxic treatment decision making in neuroblastoma patients. PATIENTS AND METHODS Gene expression profiles of 440 internationally collected neuroblastoma specimens were investigated by microarray analysis, 125 of which were examined prospectively. Patients were classified as either favorable or unfavorable by a 144-gene prediction analysis for microarrays (PAM) classifier established previously on a separate set of 77 patients. PAM classification results were compared with those of current prognostic markers and risk estimation strategies. RESULTS The PAM classifier reliably distinguished patients with contrasting clinical courses (favorable [n = 249] and unfavorable [n = 191]; 5-year event free survival [EFS] 0.84 +/- 0.03 v 0.38 +/- 0.04; 5-year overall survival [OS] 0.98 +/- 0.01 v 0.56 +/- 0.05, respectively; both P < .001). Moreover, patients with divergent outcome were robustly discriminated in both German and international cohorts and in prospectively analyzed samples (P <or= .001 for both EFS and OS for each). In subgroups with clinical low-, intermediate-, and high-risk of death from disease, the PAM predictor significantly separated patients with divergent outcome (low-risk 5-year OS: 1.0 v 0.75 +/- 0.10, P < .001; intermediate-risk: 1.0 v 0.82 +/- 0.08, P = .042; and high-risk: 0.81 +/- 0.08 v 0.43 +/- 0.05, P = .001). In multivariate Cox regression models based on both EFS and OS, PAM was a significant independent prognostic marker (EFS: hazard ratio [HR], 3.375; 95% CI, 2.075 to 5.492; P < .001; OS: HR, 11.119, 95% CI, 2.487 to 49.701; P < .001). The highest potential clinical impact of the classifier was observed in patients currently considered as non-high-risk (n = 289; 5-year EFS: 0.87 +/- 0.02 v 0.44 +/- 0.07; 5-year OS: 1.0 v 0.80 +/- 0.06; both P < .001). CONCLUSION Gene expression-based classification using the 144-gene PAM predictor can contribute to improved treatment stratification of neuroblastoma patients.

Accurate prediction of neuroblastoma outcome based on miRNA expression profiles.

For neuroblastoma, the most common extracranial tumour of childhood, identification of new biomarkers and potential therapeutic targets is mandatory to improve risk stratification and survival rates. MicroRNAs are deregulated in most cancers, including neuroblastoma. In this study, we analysed 430 miRNAs in 69 neuroblastomas by stem‐loop RT‐qPCR. Prediction of event‐free survival (EFS) with support vector machines (SVM) and actual survival times with Cox regression‐based models (CASPAR) were highly accurate and were independently validated. SVM‐accuracy for prediction of EFS was 88.7% (95% CI: 88.5–88.8%). For CASPAR‐based predictions, 5y‐EFS probability was 0.19% (95% CI: 0–38%) in the CASPAR‐predicted short survival group compared with 0.78% (95%CI: 64–93%) in the CASPAR‐predicted long survival group. Both classifiers were validated on an independent test set yielding accuracies of 94.74% (SVM) and 5y‐EFS probabilities as 0.25 (95% CI: 0.0–0.55) for short versus 1 ± 0.0 for long survival (CASPAR), respectively. Amplification of the MYCN oncogene was highly correlated with deregulation of miRNA expression. In addition, 37 miRNAs correlated with TrkA expression, a marker of excellent outcome, and 6 miRNAs further analysed in vitro were regulated upon TrkA transfection, suggesting a functional relationship. Expression of the most significant TrkA‐correlated miRNA, miR‐542‐5p, also discriminated between local and metastatic disease and was inversely correlated with MYCN amplification and event‐free survival. We conclude that neuroblastoma patient outcome prediction using miRNA expression is feasible and effective. Studies testing miRNA‐based predictors in comparison to and in combination with mRNA and aCGH information should be initiated. Specific miRNAs (e.g., miR‐542‐5p) might be important in neuroblastoma tumour biology, and qualify as potential therapeutic targets.

Accurate Outcome Prediction in Neuroblastoma across Independent Data Sets Using a Multigene Signature

Purpose: Reliable prognostic stratification remains a challenge for cancer patients, especially for diseases with variable clinical course such as neuroblastoma. Although numerous studies have shown that outcome might be predicted using gene expression signatures, independent cross-platform validation is often lacking. Experimental Design: Using eight independent studies comprising 933 neuroblastoma patients, a prognostic gene expression classifier was developed, trained, tested, and validated. The classifier was established based on reanalysis of four published studies with updated clinical information, reannotation of the probe sequences, common risk definition for training cases, and a single method for gene selection (prediction analysis of microarray) and classification (correlation analysis). Results: Based on 250 training samples from four published microarray data sets, a correlation signature was built using 42 robust prognostic genes. The resulting classifier was validated on 351 patients from four independent and unpublished data sets and on 129 remaining test samples from the published studies. Patients with divergent outcome in the total cohort, as well as in the different risk groups, were accurately classified (log-rank P < 0.001 for overall and progression-free survival in the four independent data sets). Moreover, the 42-gene classifier was shown to be an independent predictor for survival (odds ratio, >5). Conclusion: The strength of this 42-gene classifier is its small number of genes and its cross-platform validity in which it outperforms other published prognostic signatures. The robustness and accuracy of the classifier enables prospective assessment of neuroblastoma patient outcome. Most importantly, this gene selection procedure might be an example for development and validation of robust gene expression signatures in other cancer entities. Clin Cancer Res; 16(5); 1532–41

The microRNA body map: dissecting microRNA function through integrative genomics

While a growing body of evidence implicates regulatory miRNA modules in various aspects of human disease and development, insights into specific miRNA function remain limited. Here, we present an innovative approach to elucidate tissue-specific miRNA functions that goes beyond miRNA target prediction and expression correlation. This approach is based on a multi-level integration of corresponding miRNA and mRNA gene expression levels, miRNA target prediction, transcription factor target prediction and mechanistic models of gene network regulation. Predicted miRNA functions were either validated experimentally or compared to published data. The predicted miRNA functions are accessible in the miRNA bodymap, an interactive online compendium and mining tool of high-dimensional newly generated and published miRNA expression profiles. The miRNA bodymap enables prioritization of candidate miRNAs based on their expression pattern or functional annotation across tissue or disease subgroup. The miRNA bodymap project provides users with a single one-stop data-mining solution and has great potential to become a community resource.