PURPOSE: Neuroblastoma is a heterogeneous childhood tumor with poor survival outcome for the aggressive type despite intensive multimodal therapies. In this study, we aimed to identify new treatment options for neuroblastoma based on integrative genomic analysis. EXPERIMENTAL DESIGN: The Connectivity Map is a database comprising expression profiles in response to known therapeutic compounds. This renders it a useful tool in the search for potential therapeutic compounds based on comparison of gene expression profiles of diseased cells and a database of profiles in response to known therapeutic compounds. We have used this strategy in the search for new therapeutic molecules for neuroblastoma based on data of an integrative meta-analysis of gene copy number and expression profiles from 146 primary neuroblastoma tumors and normal fetal neuroblasts. RESULTS: In a first step, a 132-gene classifier was established that discriminates three major genomic neuroblastoma subgroups, reflecting inherent differences in gene expression between these subgroups. Subsequently, we screened the Connectivity Map database using gene lists generated by comparing expression profiles of fetal adrenal neuroblasts and the genomic subgroups of neuroblastomas. A putative therapeutic effect was predicted for several compounds of which six were empirically tested. A significant reduction in cell viability was shown for five of these molecules: 17-allylamino-geldanamycin, monorden, fluphenazine, trichostatin, and rapamycin. CONCLUSIONS: This proof-of-principle study indicates that an integrative genomic meta-analysis approach with inclusion of neuroblast data enables the identification of promising compounds for treatment of children with neuroblastoma. Further studies are warranted to explore in detail the therapeutic potential of these compounds.
PURPOSE:Neuroblastoma is a heterogeneous childhood tumor with poor survival outcome for the aggressive type despite intensive multimodal therapies. In this study, we aimed to identify new treatment options for neuroblastoma based on integrative genomic analysis. EXPERIMENTAL DESIGN: The Connectivity Map is a database comprising expression profiles in response to known therapeutic compounds. This renders it a useful tool in the search for potential therapeutic compounds based on comparison of gene expression profiles of diseased cells and a database of profiles in response to known therapeutic compounds. We have used this strategy in the search for new therapeutic molecules for neuroblastoma based on data of an integrative meta-analysis of gene copy number and expression profiles from 146 primary neuroblastoma tumors and normal fetal neuroblasts. RESULTS: In a first step, a 132-gene classifier was established that discriminates three major genomic neuroblastoma subgroups, reflecting inherent differences in gene expression between these subgroups. Subsequently, we screened the Connectivity Map database using gene lists generated by comparing expression profiles of fetal adrenal neuroblasts and the genomic subgroups of neuroblastomas. A putative therapeutic effect was predicted for several compounds of which six were empirically tested. A significant reduction in cell viability was shown for five of these molecules: 17-allylamino-geldanamycin, monorden, fluphenazine, trichostatin, and rapamycin. CONCLUSIONS: This proof-of-principle study indicates that an integrative genomic meta-analysis approach with inclusion of neuroblast data enables the identification of promising compounds for treatment of children with neuroblastoma. Further studies are warranted to explore in detail the therapeutic potential of these compounds.
Authors: Jinesh Gheeya; Peter Johansson; Qing-Rong Chen; Thomas Dexheimer; Belhu Metaferia; Young K Song; Jun S Wei; Jianbin He; Yves Pommier; Javed Khan Journal: Cancer Lett Date: 2010-02-04 Impact factor: 8.679
Authors: Andrea L Johnstone; Gillian W Reierson; Robin P Smith; Jeffrey L Goldberg; Vance P Lemmon; John L Bixby Journal: Mol Cell Neurosci Date: 2012-05-03 Impact factor: 4.314
Authors: Kristen M Smith; Alessandro Datti; Mayumi Fujitani; Natalie Grinshtein; Libo Zhang; Olena Morozova; Kim M Blakely; Susan A Rotenberg; Loen M Hansford; Freda D Miller; Herman Yeger; Meredith S Irwin; Jason Moffat; Marco A Marra; Sylvain Baruchel; Jeffrey L Wrana; David R Kaplan Journal: EMBO Mol Med Date: 2010-09 Impact factor: 12.137
Authors: Frida Abel; Daniel Dalevi; Maria Nethander; Rebecka Jörnsten; Katleen De Preter; Joëlle Vermeulen; Raymond Stallings; Per Kogner; John Maris; Staffan Nilsson Journal: Cancer Cell Int Date: 2011-04-14 Impact factor: 5.722
Authors: Nadine Van Roy; Katleen De Preter; Jasmien Hoebeeck; Tom Van Maerken; Filip Pattyn; Pieter Mestdagh; Joëlle Vermeulen; Jo Vandesompele; Frank Speleman Journal: Genome Med Date: 2009-07-27 Impact factor: 11.117