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  • 1
    Keywords: EXPRESSION ; SURVIVAL ; tumor ; Germany ; MODEL ; CLASSIFICATION ; DIAGNOSIS ; SYSTEM ; SYSTEMS ; COHORT ; DISEASE ; RISK ; DISTINCT ; GENE ; GENE-EXPRESSION ; microarray ; ACCURACY ; validation ; PATIENT ; BREAST-CANCER ; STAGE ; TRIAL ; TRIALS ; gene expression ; microarrays ; DELETIONS ; HIGH-RISK ; PROBES ; UNITED-STATES ; PREDICTION ; pathology ; CHILDREN ; DIFFERENTIAL EXPRESSION ; neuroblastoma ; INTEGRATION ; REGRESSION ; SIGNATURE ; PREDICTOR ; SPECIMENS ; SUBGROUPS ; PREDICTS ; SET ; CLINICAL COURSE ; 11Q
    Abstract: Purpose To develop a gene expression - based classifier for neuroblastoma patients that reliably predicts courses of the disease. Patients and Methods Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses ( n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set ( n = 174) by comparing results of the gene expression based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States. Results The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [ favorable; n = 115] v 0.52 +/- 0.07 [ unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P 〈.0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 +/- 0.04 v 0.25 +/- 0.15, P 〈.0001; intermediate-risk 1.00 v 0.57 +/- 0.19, P =.018; high-risk 0.81 +/- 0.10 v 0.56 +/- 0.08, P =.06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States ( P 〈.001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]). Conclusion Integration of gene expression - based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials
    Type of Publication: Journal article published
    PubMed ID: 17075126
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  • 2
    Keywords: tumor ; Germany ; COHORT ; GENE ; HYBRIDIZATION ; TUMORS ; PATIENT ; MARKER ; SEQUENCE ; DELETION ; STAGE ; AMPLIFICATION ; COMPARATIVE GENOMIC HYBRIDIZATION ; COPY NUMBER ; PATTERNS ; microarrays ; NUMBER ; MARKERS ; REGION ; REGIONS ; PHENOTYPE ; REVEALS ; CHILDREN ; SEGMENTS ; 1p ; neuroblastoma ; CHROMOSOMES ; SUBSET ; CYTOGENETIC ANALYSIS ; BREAKPOINTS ; MYCN-AMPLIFICATION ; function ; LOSSES ; HIGH-RESOLUTION ANALYSIS ; genomic ; GENOMIC ALTERATIONS ; 11Q ; CGH ANALYSIS ; DNA-COPY-NUMBER
    Abstract: The study of genomic alterations in neuroblastoma is of particular importance since several cytogenetic markers proved to be closely associated with the clinical phenotype. To disclose patterns of gains and losses, we performed high-resolution oligonucleotide array-based comparative genomic hybridization (aCGH). A total cohort of 90 patients was classified into 6 subsets according to tumor stage and outcome: Stages 1-3+ (with event), Stage 1-3- (no event), Stage 4+/-, and Stage 4S+/-. The aberration patterns in Stages 1-3- and 4S- tumors differed from all other groups as they were predominantly characterized by losses (3, 4, 14, X) and gains (7, 17) of whole chromosomes. However, 59/65 (91%) tumors of Stages 1-3+ or Stage 4 revealed numerous structural copy number alterations (sCNA). While deletions in chromosomes 1, 3, and I I discriminated outcome in Stage 4, there were no specific sCNA that distinguished tumor stage within the subgroup of unfavorable tumors. sCNA in 1p, 3p, 11q, 17q, or MYCN amplification (MNA) was seen among 22/24 patients who died, 10/12 with metastatic relapses, and 5/9 with local recurrences. Detailed breakpoint analyses on chromosomes 1, 3, 11, and 17 disclosed preferred breaking areas, although breakpoints were not identical. Amplifications were found in 18 patients and involved 2p24 (MYCN) and other segments of chromosome 2, as well as regions on chromosome arms 6q, 12q, and 17q. One single feature in 21q21.1 (BU678720, without known function yet) attracted particular attention since five patients showed a homozygous loss of this sequence. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat. (c) 2006 Wiley-Liss, Inc
    Type of Publication: Journal article published
    PubMed ID: 16958102
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  • 3
    Keywords: CANCER ; EXPRESSION ; Germany ; MODEL ; PATHWAY ; THERAPY ; CLASSIFICATION ; DIAGNOSIS ; COHORT ; DISEASE ; DISTINCT ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; ACCURACY ; validation ; PATIENT ; MARKER ; tumour ; STAGE ; PROGRESSION ; AMPLIFICATION ; PATTERNS ; gene expression ; meta-analysis ; PHENOTYPE ; PREDICTION ; SELECTION ; pathology ; MYCN ; neuroblastoma ; SINGLE ; ONCOLOGY ; REGRESSION ; RE ; METAANALYSIS ; analysis ; methods ; PROFILES ; EXPRESSION PROFILES ; RISK STRATIFICATION ; RARE ; SET ; PLATFORM
    Abstract: Background: Neuroblastoma patients show heterogeneous clinical courses ranging from life-threatening progression to spontaneous regression. Recently, gene expression profiles of neuroblastoma tumours were associated with clinically different phenotypes. However, such data is still rare for important patient subgroups, such as patients with MYCN non-amplified advanced stage disease. Prediction of the individual course of disease and optimal therapy selection in this cohort is challenging. Additional research effort is needed to describe the patterns of gene expression in this cohort and to identify reliable prognostic markers for this subset of patients. Methods: We combined gene expression data from two studies in a meta-analysis in order to investigate differences in gene expression of advanced stage ( 3 or 4) tumours without MYCN amplification that show contrasting outcomes ( alive or dead) at five years after initial diagnosis. In addition, a predictive model for outcome was generated. Gene expression profiles from 66 patients were included from two studies using different microarray platforms. Results: In the combined data set, 72 genes were identified as differentially expressed by meta-analysis at a false discovery rate (FDR) of 8.33%. Meta-analysis detected 34 differentially expressed genes that were not found as significant in either single study. Outcome prediction based on data of both studies resulted in a predictive accuracy of 77%. Moreover, the genes that were differentially expressed in subgroups of advanced stage patients without MYCN amplification accurately separated MYCN amplified tumours from low stage tumours without MYCN amplification. Conclusion: Our findings support the hypothesis that neuroblastoma consists of two biologically distinct subgroups that differ by characteristic gene expression patterns, which are associated with divergent clinical outcome
    Type of Publication: Journal article published
    PubMed ID: 17531100
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  • 4
    Keywords: STAGE ; PROGRESSION ; AMPLIFICATION ; chemotherapy ; DELETIONS ; SPONTANEOUS REGRESSION ; PREDICTION ; pathology ; N-MYC ; EXPRESSION-BASED CLASSIFICATION
    Abstract: Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers. Material and Methods: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4x44K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n=634) by Kaplan-Meier estimates and Cox regression analyses. Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity 0.93, specificity 0.97) in the validation cohort. The highest potential clinical value of this predictor was observed for current low-risk patients (LR: 5-year EFS 0.84+/-0.02 vs 0.29+/-0.10; 5-year OS 0.99+/-0.01vs 0.76+/-0.11; both p〈0.001) and intermediate-risk patients (IR: 5-year EFS 0.88+/-0.06 vs 0.41+/-0.10; 5-year OS 1.0 vs 0.70+/-0.09; both p〈0.001). In multivariate Cox regression models for LR/IR patients the classifier outperformed risk assessment of the current German trial NB2004 (EFS: HR 5.07, 95%-CI 3.20-8.02, OS: HR 25.54, 95%-CI 8.40-77.66; both p〈0.001). Based on these findings, we propose to integrate the classifier into a revised risk stratification system for LR/IR patients. According to this system, we identified novel subgroups with poor outcome (5-year EFS 0.19+/-0.08; 5-year OS 0.59+/-0.1), for whom we propose intensified treatment, and with beneficial outcome (5-year EFS 0.87+/-0.05; 5-year OS 1.0), who may benefit from treatment de-escalation. Conclusion: Combination of gene expression-based classification and established prognostic markers improves risk estimation of LR/IR neuroblastoma patients. We propose to implement our revised treatment stratification system in a prospective clinical trial.
    Type of Publication: Journal article published
    PubMed ID: 25231397
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  • 5
    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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  • 6
    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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  • 7
    Keywords: PREDICT ; USA ; analysis ; microarray ; RNA ; RISK ; CLASSIFICATION ; BLOOD ; CANCER ; BIOLOGY ; STAGE ; AGE ; ONCOLOGY ; neuroblastoma ; MICROARRAY ANALYSIS
    Type of Publication: Meeting abstract published
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  • 8
    Keywords: CANCER ; EXPRESSION ; SURVIVAL ; Germany ; IN-VIVO ; ALGORITHM ; CLASSIFICATION ; COHORT ; DISEASE ; GENE ; GENE-EXPRESSION ; microarray ; ACCURACY ; validation ; TIME ; PATIENT ; MARKER ; CELL-LINES ; STAGE ; TRIAL ; AMPLIFICATION ; gene expression ; DESIGN ; MARKERS ; HIGH-RISK ; PROGNOSTIC-SIGNIFICANCE ; PREDICTION ; experimental design ; CHILDREN ; DIFFERENTIAL EXPRESSION ; GASTRIC-CANCER ; neuroblastoma ; N-MYC ; ONCOLOGY ; RE ; overall survival ; SURVIVORS ; PROFILES ; EXPRESSION PROFILES ; USA ; survival time ; oligonucleotide microarray ; cancer research ; SET ; PREDICT ; cancer survival ; PROFILE ; outcome ; expression profile ; CHROMOSOME ARM 17Q ; ANAPLASTIC LYMPHOMA KINASE ; INVASIVE BREAST-CANCER
    Abstract: Purpose: To predict individual survival times for neuroblastoma patients from gene expression data using the cancer survival prediction using automatic relevance determination (CASPAR) algorithm. Experimental Design: A first set of oligonucleotide microarray gene expression profiles comprising 256 neuroblastoma patients was generated. Then, CASPAR was combined with a leave-one-out cross-validation to predict individual times for both the whole cohort and subgroups of patients with unfavorable markers, including stage 4 disease (n = 67), unfavorable genetic alterations, intermediate-risk or high-risk stratification by the German neuroblastoma trial, and patients predicted as unfavorable by a recently described gene expression classifier (n = 83). Prediction accuracy of individual survival times was assessed by Kaplan-Meier analyses and time-dependent receiver operator characteristics curve analyses. Subsequently, classification results were validated in an independent cohort (n = 120). Results: CASPAR separated patients with divergent outcome in both the initial and the validation cohort [initial set, 5y-OS 0.94 +/- 0.04 (predicted long survival) versus 0.38 +/- 0.17 (predicted short survival), P 〈 0.0001; validation cohort, 5y-OS 0.94 +/- 0.07 (long) versus 0.40 +/- 0.13 (short), P 〈 0.0001]. Time-dependent receiver operator characteristics analyses showed that CASPAR-predicted individual survival times were highly accurate (initial set, mean area under the curve for first 10 years of overall survival prediction 0.92 +/- 0.04; validation set, 0.81 +/- 0.05). Furthermore, CASPAR significantly discriminated short (〈5 years) from long survivors (〉5 years) in subgroups of patients with unfavorable markers with the exception of MYCN-amplified patients (initial set). Confirmatory results with high significance were observed in the validation cohort [stage 4 disease (P = 0.0049), NB2004 intermediate-risk or high-risk stratification (P = 0.0017), and unfavorable gene expression prediction (P = 0.0017)]. Conclusions: CASPAR accurately forecasts individual survival times for neuroblastoma patients from gene expression data
    Type of Publication: Journal article published
    PubMed ID: 18927300
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