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Published on: February 22, 2015
Reversal gene expression assessment for drug repurposing, a case study of glioblastoma
Shixue Sun1, Zeenat Shyr2, Kathleen McDaniel2
1Informatics Core, Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.
Background:
Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement.
Methods:
This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database.
Results:
We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFC) that the drug candidates could induce. Among five prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells.
Conclusions:
The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.
Insights
This study developed a computational pipeline to identify potential glioblastoma (GBM) drugs. Clofarabine and Ciclopirox were validated as effective in targeting GBM cancer cells, offering hope for rare disease drug development.
Area of Science:
- Computational biology
- Genomics
- Drug discovery
Background:
- Glioblastoma (GBM) is a rare, aggressive brain cancer with poor prognosis.
- Existing therapies offer limited survival benefits, highlighting the need for novel treatments.
- Understanding GBM's complex biology is crucial for effective drug development.
Purpose of the Study:
- To develop a novel computational pipeline for drug repurposing in GBM.
- To identify existing drugs that can reverse GBM's gene expression profile.
- To address challenges in rare disease drug development.
Main Methods:
- Utilized gene expression data analysis for GBM drug repurposing.
- Constructed a GBM Gene Expression Profile (GGEP) using multi-omics data.
- Screened the iLINCS database for drugs reversing GGEP signatures.
Main Results:
- Prioritized drug candidates using hierarchical clustering and self-defined indices.
- Identified five prioritized drug candidates based on expression signature reversal.
- In-vitro experiments validated Clofarabine and Ciclopirox for selective GBM cell targeting.
Conclusions:
- The computational pipeline offers a promising approach for accelerating drug development.
- Uncovered gene expression effects between drugs and diseases can be leveraged for therapeutic discovery.
- This strategy can be extended to other rare and non-rare diseases.
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