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Transposon Mediated Integration of Plasmid DNA into the Subventricular Zone of Neonatal Mice to Generate Novel Models of Glioblastoma
Published on: February 22, 2015
Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse
Yang Chen1, Zhen Gao1, Bingcheng Wang2
1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA.
Background:
Glioblastoma (GBM) is the most common and aggressive brain tumors. It has poor prognosis even with optimal radio- and chemo-therapies. Since GBM is highly heterogeneous, drugs that target on specific molecular profiles of individual tumors may achieve maximized efficacy. Currently, the Cancer Genome Atlas (TCGA) projects have identified hundreds of GBM-associated genes. We develop a drug repositioning approach combining disease genomics and mouse phenotype data towards predicting targeted therapies for GBM.
Methods:
We first identified disease specific mouse phenotypes using the most recently discovered GBM genes. Then we systematically searched all FDA-approved drugs for candidates that share similar mouse phenotype profiles with GBM. We evaluated the ranks for approved and novel GBM drugs, and compared with an existing approach, which also use the mouse phenotype data but not the disease genomics data.
Results:
We achieved significantly higher ranks for the approved and novel GBM drugs than the earlier approach. For all positive examples of GBM drugs, we achieved a median rank of 9.2 45.6 of the top predictions have been demonstrated effective in inhibiting the growth of human GBM cells.
Conclusion:
We developed a computational drug repositioning approach based on both genomic and phenotypic data. Our approach prioritized existing GBM drugs and outperformed a recent approach. Overall, our approach shows potential in discovering new targeted therapies for GBM.
Insights
We developed a new computational method to find targeted therapies for glioblastoma (GBM) by combining genetic and mouse data. This approach successfully identified effective drugs, outperforming previous methods for this aggressive brain cancer.
Area of Science:
- Computational biology
- Genomics
- Drug discovery
Background:
- Glioblastoma (GBM) is a highly aggressive brain tumor with a poor prognosis.
- GBM's heterogeneity necessitates personalized treatment strategies targeting specific molecular profiles.
- The Cancer Genome Atlas (TCGA) has identified numerous GBM-associated genes.
Purpose of the Study:
- To develop a novel drug repositioning approach for GBM.
- To predict targeted therapies by integrating disease genomics and mouse phenotype data.
- To improve the efficacy of GBM treatments through computational methods.
Main Methods:
- Identified GBM-specific mouse phenotypes using newly discovered GBM genes.
- Searched for FDA-approved drugs with similar phenotype profiles to GBM.
- Compared the performance of the new approach with an existing method using only mouse phenotype data.
Main Results:
- The developed approach achieved significantly higher rankings for GBM drugs compared to the existing method.
- A median rank of 9.2 was achieved for positive GBM drug examples.
- 45.6% of top predictions demonstrated effectiveness in inhibiting human GBM cell growth.
Conclusions:
- A computational drug repositioning strategy integrating genomic and phenotypic data was successfully developed.
- The new approach effectively prioritized existing GBM drugs and outperformed a recent comparative method.
- This strategy shows promise for discovering novel targeted therapies for glioblastoma.
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