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 approximately 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<or=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.
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 approximately 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<or=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.
Authors: Jennifer C Kasemeier-Kulesa; Santiago Schnell; Thomas Woolley; Jennifer A Spengler; Jason A Morrison; Mary C McKinney; Irina Pushel; Lauren A Wolfe; Paul M Kulesa Journal: Biophys Chem Date: 2018-04-30 Impact factor: 2.352
Authors: Andrea Cornero; Massimo Acquaviva; Paolo Fardin; Rogier Versteeg; Alexander Schramm; Alessandra Eva; Maria Carla Bosco; Fabiola Blengio; Sara Barzaghi; Luigi Varesio Journal: BMC Bioinformatics Date: 2012-03-28 Impact factor: 3.169
Authors: Kristoffer von Stedingk; Jan Koster; Marta Piqueras; Rosa Noguera; Samuel Navarro; Sven Påhlman; Rogier Versteeg; Ingrid Ora; David Gisselsson; David Lindgren; Håkan Axelson Journal: Transl Oncol Date: 2013-08-01 Impact factor: 4.243
Authors: Paolo Fardin; Andrea Cornero; Annalisa Barla; Sofia Mosci; Massimo Acquaviva; Lorenzo Rosasco; Claudio Gambini; Alessandro Verri; Luigi Varesio Journal: J Biomed Biotechnol Date: 2010-06-28
Authors: Kaisa Thorell; Annika Bergman; Helena Carén; Staffan Nilsson; Per Kogner; Tommy Martinsson; Frida Abel Journal: BMC Med Genomics Date: 2009-08-17 Impact factor: 3.063