Meta-mining of Neuroblastoma and Neuroblast Gene Expression Profiles Reveals Candidate Therapeutic Compounds

• Published in: Clinical cancer research Vol. 15; no. 11; pp. 3690 - 3696
• Main Authors: DE PRETER, Katleen, DE BROUWER, Sara, VAN MAERKEN, Tom, PATTYN, Filip, SCHRAMM, Alexander, EGGERT, Angelika, VANDESOMPELE, Jo, SPELEMAN, Frank
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA American Association for Cancer Research 01.06.2009
• Subjects: Antineoplastic agents; Antineoplastic Agents - pharmacology; Biological and medical sciences; Cell Line, Tumor; Cell Survival - drug effects; Connectivity Map; Gene Expression Profiling - classification; Gene Expression Profiling - statistics & numerical data; Genomics - methods; Humans; integrative genomics; Medical sciences; Meta-Analysis as Topic; neuroblast; neuroblastoma; Neuroblastoma - classification; Neuroblastoma - genetics; Neuroblastoma - pathology; Neurology; Neurons - metabolism; Pharmacology. Drug treatments; therapeutic compounds; Tumors of the nervous system. Phacomatoses; Nervous system diseases; Treatment; Malignant tumor; Neuroblastoma; Autonomic neuropathy; Cancer; Gene expression profile
• ISSN: 1078-0432; 1557-3265
• DOI: 10.1158/1078-0432.CCR-08-2699

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.