- Poster presentation
- Open Access
A genetic survival network for glioblastoma multiforme
© BioMed Central Ltd 2011
- Published: 19 September 2011
- Comparative Genomic Hybridization
- Glioblastoma Multiforme
- Brain Cancer
- Array Comparative Genomic Hybridization
- Radiation Hybrid
Most studies exploring cancer progression have focused on the influence of individual genes, and few efforts have investigated the effects of interactions between genes within the genome. Our hypothesis is that cancer cells thrive by exploiting combinations of genes, in fact by exploiting networks of genes that both protect the cell against destruction and enhance its survival. We believe that these networks involve genes that tend to be coordinated in their copy number alterations, even when they are located at a distance in the genome. Radiation hybrid (RH) cells have a random assortment of genes as triploid rather than diploid. Our recent work studying genetic networks in libraries of RH cells has elucidated key survival-enhancing interactions with high specificity . Because of the hardiness of the RH clones, statistically significant patterns of co-inherited, unlinked triploid gene pairs pointed to the cell survival mechanism. We identified more than 7.2 million significant interactions at single-gene resolution using the RH data.
Our work with the RH data provided the rationale for an investigation of cancer survival networks, in particular for glioblastoma multiforme, a formidable brain cancer for which extensive datasets are available but few treatment options. We investigated correlated patterns of copy number alterations for distant genes in glioblastoma multiforme tumors using the same method we employed to construct the RH survival network. Public data were analyzed from 301 glioblastomas that had been assessed for copy number alterations using array comparative genomic hybridization .
The glioblastoma and RH survival networks overlapped significantly (P = 3.7 × 10–31). We therefore exploited the high-resolution mapping of the RH data to obtain single-gene specificity in the glioblastoma network. The combined network features 5,439 genes and 13,846 interactions (false discovery rate (FDR) <5%) and suggests novel approaches to therapy for glioblastoma. For example, although the epidermal growth-factor receptor (EGFR) oncogene is frequently activated in glioblastoma, EGFR inhibitors have limited therapeutic efficacy . In the combined glioblastoma survival network, there are 46 genes that interact with EGFR, of which ten (22%) happen to be targets of existing drugs. This observation suggests that a flanking attack strategy that strikes at both EGFR and its partner genes in the glioblastoma survival network may be an effective approach to treating these tumors.
By elucidating a genetic survival network for glioblastoma, we gained insight into the mechanisms of proliferation of this cancer and opened up new avenues for therapeutic intervention.
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