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  • 11
    Keywords: RECEPTOR ; APOPTOSIS ; CELLS ; EXPRESSION ; SURVIVAL ; tumor ; TUMOR-CELLS ; CELL ; human ; KINASE ; PATHWAY ; PATHWAYS ; TYROSINE KINASE ; COHORT ; DEATH ; LONG-TERM ; GENE ; DIFFERENTIATION ; TUMORS ; NEUROBLASTOMA-CELLS ; PATIENT ; ACTIVATION ; MECHANISM ; DOMAIN ; BINDING ; CELL-DEATH ; REGION ; LONG-TERM SURVIVAL ; specificity ; DOMAINS ; neuroblastoma ; signaling ; NEURONS ; medulloblastoma ; interaction ; LEVEL ; cell death ; TECHNOLOGY ; USA ; pediatric ; MEDIATOR ; TYROSINE ; 2-HIT MECHANISM ; CEREBRAL CAVERNOUS MALFORMATIONS ; P75 NEUROTROPHIN RECEPTOR
    Abstract: The TrkA receptor tyrosine kinase is crucial for differentiation and survival of nerve-growth-factor-dependent neurons. Paradoxically, TrkA also induces cell death in pediatric tumor cells of neural origin, via an unknown mechanism. Here, we show that CCM2, a gene product associated with cerebral cavernous malformations, interacts with the juxtamembrane region of TrkA via its phosphotyrosine binding (PTB) domain and mediates TrkA-induced death in diverse cell types. Both the PTB and Karet domains of CCM2 are required for TrkA-dependent cell death, such that the PTB domain determines the specificity of the interaction, and the Karet domain links to death pathways. Downregulation of CCM2 in medulloblastoma or neuroblastoma cells attenuates TrkA-dependent death. Combined high expression levels of CCM2 and TrkA are correlated with long-term survival in a large cohort of human neuroblastoma patients. Thus, CCM2 is a key mediator of TrkA-dependent cell death in pediatric neuroblastic tumors
    Type of Publication: Journal article published
    PubMed ID: 19755102
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  • 12
    Keywords: tumor ; Germany ; PATHWAY ; PATHWAYS ; CLASSIFICATION ; DISEASE ; RISK ; GENE-EXPRESSION ; microarray ; TUMORS ; ACCURACY ; PATIENT ; MARKER ; PROGRESSION ; COMPARATIVE GENOMIC HYBRIDIZATION ; gene expression ; MUTATION ; TUMOR PROGRESSION ; TUMOR-SUPPRESSOR GENE ; SIGNALING PATHWAY ; SIGNALING PATHWAYS ; MUTATIONS ; CHILDREN ; BEHAVIOR ; neuroblastoma ; N-MYC ; signaling ; review ; GENE-EXPRESSION PROFILES ; ACCURATE ; MOLECULAR CLASSIFICATION ; STAGE NEUROBLASTOMA ; ARRAY-CGH ; outcome ; CHROMOSOME ARM 17Q ; pediatric oncology ; HIGH-RISK NEUROBLASTOMAS ; Genetic ; therapeutic ; STRATEGY ; 4S NEUROBLASTOMA ; CHILDRENS ONCOLOGY GROUP ; embryonal tumors ; METASTATIC NEUROBLASTOMA ; oncogenomics
    Abstract: For many decades, neuroblastoma has remained a challenging disease for both clinicians and researchers. Now, techniques that efficiently specify both comprehensive genetic and gene-expression alterations of neuroblastoma tumors have provided molecular markers that indicate tumor behavior and patient outcome with very high accuracy, Once the anticipated value of these markers has been confirmed in ongoing studies, patients may profit from more accurate risk assessment by integrating these markers into clinical routine. Moreover, disclosing further tumor-initiating events, such as the recently revealed oncogenic mutations of ALK, will further promote the elucidation of the genetic etiology of the disease. Together with recent information on altered signaling pathways in aggressively growing tumors, this knowledge will help to establish therapeutic strategies specifically targeting molecular key factors of neuroblastoma tumor progression
    Type of Publication: Journal article published
    PubMed ID: 19519203
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  • 13
    Keywords: MYCN c-MYC neuroblastoma cancer
    Abstract: Background Amplified MYCN oncogene resulting in deregulated MYCN transcriptional activity is observed in 20% of neuroblastomas and identifies a highly aggressive subtype. In MYCN single-copy neuroblastomas, elevated MYCN mRNA and protein levels are paradoxically associated with a more favorable clinical phenotype, including disseminated tumors that subsequently regress spontaneously (stage 4s-non-amplified). In this study, we asked whether distinct transcriptional MYCN or c-MYC activities are associated with specific neuroblastoma phenotypes. Results We defined a core set of direct MYCN/c-MYC target genes by applying gene expression profiling and chromatin immunoprecipitation (ChIP, ChIP-chip) in neuroblastoma cells that allow conditional regulation of MYCN and c-MYC. Their transcript levels were analyzed in 251 primary neuroblastomas. Compared to localized-non-amplified neuroblastomas, MYCN/c-MYC target gene expression gradually increases from stage 4s-non-amplified through stage 4-non-amplified to MYCN amplified tumors. This was associated with MYCN activation in stage 4s-non-amplified and predominantly c-MYC activation in stage 4-non-amplified tumors. A defined set of MYCN/c-MYC target genes was induced in stage 4-non-amplified but not in stage 4s-non-amplified neuroblastomas. In line with this, high expression of a subset of MYCN/c-MYC target genes identifies a patient subtype with poor overall survival independent of the established risk markers amplified MYCN, disease stage, and age at diagnosis. Conclusions High MYCN/c-MYC target gene expression is a hallmark of malignant neuroblastoma progression, which is predominantly driven by c-MYC in stage 4-non-amplified tumors. In contrast, moderate MYCN function gain in stage 4s-non-amplified tumors induces only a restricted set of target genes that is still compatible with spontaneous regression.
    Type of Publication: Journal article published
    PubMed ID: 18851746
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  • 14
    Keywords: GENE-EXPRESSION ; DIFFERENTIATION ; BREAST-CANCER ; REPRODUCIBILITY ; PROSTATE-CANCER ; SIGNATURE ; RISK STRATIFICATION ; transcriptome ; EXPRESSION-BASED CLASSIFICATION ; NEUROBLASTOMA PATIENTS
    Abstract: BACKGROUND: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. RESULTS: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. CONCLUSIONS: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.
    Type of Publication: Journal article published
    PubMed ID: 26109056
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  • 15
    Keywords: CANCER ; PATHWAY ; ENZYMES ; GENE-EXPRESSION ; BIOLOGY ; resistance ; C-MYC ; N-MYC ; CARRIER ; ORNITHINE-DECARBOXYLASE
    Abstract: MYCN amplification occurs in 20% of neuroblastomas and is strongly related to poor clinical outcome. We have identified folate-mediated one-carbon metabolism as highly upregulated in neuroblastoma tumors with MYCN amplification and have validated this finding experimentally by showing that MYCN amplified neuroblastoma cell lines have a higher requirement for folate and are significantly more sensitive to the antifolate methotrexate than cell lines without MYCN amplification. We have demonstrated that methotrexate uptake in neuroblastoma cells is mediated principally by the reduced folate carrier (RFC; SLC19A1), that SLC19A1 and MYCN expression are highly correlated in both patient tumors and cell lines, and that SLC19A1 is a direct transcriptional target of N-Myc. Finally, we assessed the relationship between SLC19A1 expression and patient survival in two independent primary tumor cohorts and found that SLC19A1 expression was associated with increased risk of relapse or death, and that SLC19A1 expression retained prognostic significance independent of age, disease stage and MYCN amplification. This study adds upregulation of folate-mediated one-carbon metabolism to the known consequences of MYCN amplification, and suggests that this pathway might be targeted in poor outcome tumors with MYCN amplification and high SLC19A1 expression.
    Type of Publication: Journal article published
    PubMed ID: 25860940
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  • 16
  • 17
    Keywords: EXPRESSION ; SURVIVAL ; tumor ; CELL LUNG-CANCER ; Germany ; CLASSIFICATION ; DISEASE ; RISK ; DISTINCT ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; transcription ; DIFFERENTIATION ; TUMORS ; PATIENT ; MARKER ; STAGE ; IDENTIFICATION ; AMPLIFICATION ; PATTERNS ; gene expression ; microarrays ; DESIGN ; AGE ; PCR ; TUMOR-SUPPRESSOR GENE ; PREDICTION ; CHILDREN ; MUTANT MICE ; DIFFERENTIAL EXPRESSION ; neuroblastoma ; CANDIDATE GENES ; REVERSE TRANSCRIPTION-PCR ; LEVEL ; TARGET GENES ; SUBTYPES ; PROFILES ; EXPRESSION PATTERNS ; SIGNATURE ; DEVELOPMENTAL EXPRESSION ; CANDIDATE ; VARIABLES ; SUBGROUPS ; CANNABINOID RECEPTOR ; MICROTUBULE-ASSOCIATED PROTEIN ; NICOTINIC RECEPTORS ; STAGE NEUROBLASTOMA ; SYNAPSIN-III
    Abstract: Purpose: Identification of molecular characteristics of spontaneously regressing stage IVS and progressing stage IV neuroblastoma to improve discrimination of patients with metastatic disease following favorable and unfavorable clinical courses. Experimental Design: Serial analysis of gene expression profiles were generated from five stage IVS and three stage IV neuroblastoma. Differential expression of candidate genes was evaluated by real-time quantitative reverse transcription-PCR in 76 pretreatment tumor samples (stage IVS n = 27 and stage IV n = 49). Gene expression-based outcome prediction was determined by Prediction Analysis for Microarrays using 38 tumors as a training set and 38 tumors as a test set. Results: Comparison of serial analysis of gene expression profiles from stage IV and IVS neuroblastoma revealed similar to 500 differentially expressed transcripts, Genes related to neuronal differentiation were observed more frequently in stage IVS tumors as determined by associating transcripts to Gene Ontology annotations. Forty-one candidate genes were evaluated by quantitative reverse transcription- PCR and 18 were confirmed to be differentially expressed (P 〈= 0.001). Classification of patients according to expression patterns of these 18 genes using Prediction Analysis for Microarrays discriminated two subgroups with significantly differing event-free survival (96 +/- 6% versus 40 +/- 8% at 3 years; P 〈 0.0001) and overall survival (100% versus 72 +/- 7 % at 3 years; P = 0.0003). This classifier was the only independent covariate marker in a multivariate analysis considering the variables stage, age, MYCN amplification, and gene signature. Conclusions: Spontaneously regressing and progressing metastatic neuroblastoma differ by specific gene expression patterns, indicating distinct levels of neuronal differentiation and allowing for an improved risk estimation of children with disseminated disease
    Type of Publication: Journal article published
    PubMed ID: 16951229
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  • 18
    Keywords: CANCER ; EXPRESSION ; SURVIVAL ; CLASSIFICATION ; DIAGNOSIS ; INFORMATION ; SYSTEM ; COHORT ; DISEASE ; RISK ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; validation ; PATIENT ; prognosis ; SEQUENCE ; SEQUENCES ; IDENTIFICATION ; AMPLIFICATION ; gene expression ; MICROARRAY DATA ; microarrays ; DESIGN ; NUMBER ; CANCER-PATIENTS ; PREDICTION ; SELECTION ; CANCER PATIENTS ; neuroblastoma ; ONCOLOGY ; GENE-EXPRESSION PROFILES ; development ; prospective ; RISK STRATIFICATION ; outcome ; SIGNATURES ; EXPRESSION SIGNATURES ; STRATIFICATION
    Abstract: 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. (C)2010 AACR
    Type of Publication: Journal article published
    PubMed ID: 20179214
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  • 19
    Keywords: EXPRESSION ; SURVIVAL ; Germany ; MODEL ; MODELS ; CLASSIFICATION ; DIAGNOSIS ; COHORT ; DEATH ; DISEASE ; MORTALITY ; RISK ; GENE ; GENE-EXPRESSION ; microarray ; PATIENT ; MARKER ; IMPACT ; STAGE ; AMPLIFICATION ; gene expression ; microarrays ; AGE ; MARKERS ; HIGH-RISK ; STRATEGIES ; SPONTANEOUS REGRESSION ; PREDICTION ; INFANTS ; pathology ; CHILDREN ; MICROARRAY ANALYSIS ; neuroblastoma ; ONCOLOGY ; REGRESSION ; overall survival ; INDEPENDENT PROGNOSTIC MARKER ; methods ; PROGNOSTIC MARKER ; PROFILES ; EXPRESSION PROFILES ; RISK STRATIFICATION ; SUBGROUPS ; MYC ; PROFILE ; outcome ; STRATEGY ; CONTRIBUTE ; COHORTS ; COX REGRESSION ; clinical oncology ; STRATIFICATION ; prognostic
    Abstract: 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 = .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; intermediaterisk: 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
    Type of Publication: Journal article published
    PubMed ID: 20567016
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  • 20
    Keywords: CANCER ; EXPRESSION ; COMBINATION ; LUNG ; MODEL ; MODELS ; TOXICITY ; CLASSIFICATION ; liver ; GENE ; GENE-EXPRESSION ; microarray ; validation ; QUALITY ; BREAST ; breast cancer ; BREAST-CANCER ; PERFORMANCE ; gene expression ; MICROARRAY DATA ; HUMANS ; microarrays ; PREDICTION ; PROJECT ; FOLLICULAR LYMPHOMA ; MULTIPLE-MYELOMA ; rodent ; neuroblastoma ; development ; methods ; GENE-EXPRESSION DATA ; DNA MICROARRAYS ; rodents ; RECOMMENDATIONS ; EXPRESSION DATA ; CONTROL MAQC PROJECT ; PUBLISHED MICROARRAY ; RISK-STRATIFICATION
    Abstract: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, 〉30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis
    Type of Publication: Journal article published
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