Meta-mining of neuroblastoma and neuroblast gene expression profiles reveals candidate therapeutic compounds.
OBJECTIVE
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.


METHODS
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.
De Preter
Katleen
K
Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium. Katleen.DePreter@UGent.be
De Brouwer
Sara
S
Van Maerken
Tom
T
Pattyn
Filip
F
Schramm
Alexander
A
Eggert
Angelika
A
Vandesompele
Jo
J
Speleman
Frank
F
D000970:Antineoplastic Agents / Q000494:pharmacology*; D045744:Cell Line, Tumor; D002470:Cell Survival / Q000187:drug effects; D020869:Gene Expression Profiling / Q000145:classification / Q000706:statistics & numerical data*; D023281:Genomics / Q000379:methods; D006801:Humans; D015201:Meta-Analysis as Topic; D009447:Neuroblastoma / Q000145:classification / Q000235:genetics* / Q000473:pathology; D009474:Neurons / Q000378:metabolism
D000970:Antineoplastic Agents