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  • PREDICTION  (11)
  • 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: 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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  • 3
    Keywords: EXPRESSION ; tumor ; evaluation ; Germany ; THERAPY ; DIAGNOSIS ; SYSTEM ; SYSTEMS ; DISEASE ; RISK ; DISTINCT ; GENE ; GENE-EXPRESSION ; PATIENT ; DNA ; MARKER ; TRIAL ; TRIALS ; PROGRESSION ; COMPARATIVE GENOMIC HYBRIDIZATION ; PATTERNS ; gene expression ; NUMBER ; AGE ; ABERRATIONS ; MARKERS ; ONCOGENE ; PHENOTYPE ; PROGNOSTIC-SIGNIFICANCE ; PREDICTION ; CHILDREN ; neuroblastoma ; N-MYC ; molecular ; REGRESSION ; review ; PATTERN ; THERAPIES ; PROTOCOL ; PROGNOSTIC MARKER ; PROFILES ; EXPRESSION PROFILES ; LOSSES ; PEDIATRIC-ONCOLOGY-GROUP ; VARIABLES ; PREDICT ; pediatric ; STAGE NEUROBLASTOMA ; PROFILE ; pediatrics ; neoplasm ; MICRORNA EXPRESSION ; CHROMOSOME ARM 17Q ; CLINICAL RELEVANCE ; genomic aberration ; MYC GENE AMPLIFICATION ; risk estimation ; TUMOR-CELL PLOIDY
    Abstract: The pediatric tumor neuroblastoma is a heterogeneous disease: Patients' clinical courses can range from spontaneous regression to fatal progression of the disease. Accordingly, treatment protocols vary from "wait and see" approaches to intensive multimodal therapies. Accurate risk estimation of the patients is therefore mandatory to choose the most adequate therapy. Current trials stratify by a limited number of clinical variables, such as stage of the disease and age of the patient at diagnosis, as well as molecular markers, such as amplification of the oncogene MYCN and loss of the short arm of chromosome 1. However, misclassifications of patients still occur, and thus, a precise prediction of the clinical courses remains a challenge of neuroblastoma research. In recent years, genomic alterations and gene expression profiles of this neoplasm have been characterized thoroughly. It has been shown that the diverse clinical phenotypes are reflected by both specific cytogenetic aberrations and distinct gene expression patterns. Moreover, a variety of DNA copy number changes and gene expression-based classifiers have been described that could predict the outcome of neuroblastoma patients more precisely than established prognostic variables. In this review, the recent advances in the detection and evaluation of molecular prognostic markers for neuroblastoma patients are summarized, and their current and potential contribution to risk stratification systems is discussed
    Type of Publication: Journal article published
    PubMed ID: 18478485
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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: 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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  • 7
    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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  • 8
    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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  • 9
    Keywords: CANCER ; EXPRESSION ; tumor ; Germany ; CLASSIFICATION ; DISEASE ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; DIFFERENTIATION ; ACCURACY ; PATIENT ; DNA ; MESSENGER-RNA ; MARKER ; IMPACT ; prognosis ; gene expression ; microarrays ; MARKERS ; Jun ; PREDICTION ; sensitivity ; REVEALS ; CHILDREN ; DIFFERENTIAL EXPRESSION ; MYCN ; neuroblastoma ; NEURO-BLASTOMA ; ONCOLOGY ; PATTERN ; PROGNOSTIC IMPACT ; analysis ; CANDIDATE ; STAGE NEUROBLASTOMA ; CANDIDATES ; outcome ; IMPROVED SURVIVAL PROBABILITY ; supervised classification
    Abstract: Currently, Pubmed lists 385 marker genes for neuroblastoma outcome. Using a customized neuroblastoma-microarray, we evaluated the prognostic impact of the gene-expression pattern of 349 of these candidates (90.6%) in 127 neuroblastoma patients with divergent outcome. By significance analysis of microarrays (SAM) and both uncorrected and Bonferroni-corrected ANOVA, 166/349 (47.5%), 218/349 (62.5%) and 128/349 (36.4%) candidates showed significant differential expression between patients with contrasting outcome. By Prediction Analysis for Microarrays (PAM), a 38-gene-classifier was derived from all markers, which classified patients outcome with an overall accuracy of 78.5%. However, patients with unfavorable outcome of MYCN non-amplified disease were largely misclassified (accuracy: 35%), suggesting that these courses are not identified by current marker genes. (c) 2006 Elsevier Ireland Ltd. All rights reserved
    Type of Publication: Journal article published
    PubMed ID: 17126996
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  • 10
    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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