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Identifying Gene Signatures for Cancer Drug Repositioning Based on Sample Clustering
Abstract:
Drug repositioning is an important approach for drug discovery. Computational drug repositioning approaches typically use a gene signature to represent a particular disease and connect the gene signature with drug perturbation profiles. Although disease samples, especially from cancer, may be heterogeneous, most existing methods consider them as a homogeneous set to identify differentially expressed genes (DEGs)for further determining a gene signature. As a result, some genes that should be in a gene signature may be averaged off. In this study, we propose a new framework to identify gene signatures for cancer drug repositioning based on sample clustering (GS4CDRSC). GS4CDRSC first groups samples into several clusters based on their gene expression profiles. Second, an existing method is applied to the samples in each cluster for generating a list of DEGs. Then a weighting approach is used to identify an intergrated gene signature from all the lists of DEGs. The integrated gene signature is used to connect with drug perturbation profiles in the Connectivity Map (CMap)database to generate a list of drug candidates. GS4CDRSC has been tested with several cancer datasets and existing methods. The computational results show that GS4CDRSC outperforms those methods without the sample clustering and weighting approaches in terms of both number and rate of predicted known drugs for specific cancers.
Insights
This study introduces a new framework for cancer drug repositioning by clustering patient samples. This approach improves the identification of gene signatures, leading to more accurate drug candidate predictions compared to existing methods.
Area of Science:
- Computational biology
- Genomics
- Drug discovery
Background:
- Drug repositioning accelerates drug discovery by identifying new uses for existing drugs.
- Current computational methods often overlook cancer sample heterogeneity, potentially missing key disease-related genes.
Purpose of the Study:
- To develop a novel framework, Gene Signature for Cancer Drug Repositioning based on Sample Clustering (GS4CDRSC), to address cancer sample heterogeneity.
- To improve the accuracy of gene signature identification for computational drug repositioning.
Main Methods:
- GS4CDRSC clusters samples based on gene expression profiles.
- Differentially expressed genes (DEGs) are identified within each cluster.
- A weighting approach integrates DEG lists to form a comprehensive gene signature.
- The gene signature is matched with drug perturbation profiles in the Connectivity Map (CMap) database.
Main Results:
- GS4CDRSC was evaluated on multiple cancer datasets.
- The framework demonstrated superior performance over methods lacking sample clustering and weighting.
- Improved identification of known drugs for specific cancers was observed.
Conclusions:
- GS4CDRSC effectively handles cancer sample heterogeneity for improved gene signature identification.
- The proposed framework enhances the prediction of potential drug candidates for cancer repositioning.
- This approach offers a more robust strategy for computational drug repositioning in oncology.
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