Dataset Information

Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery.

ABSTRACT: Screening for drug compounds that exhibit therapeutic properties in the treatment of various diseases remains a challenge even after considerable advancements in biomedical research. Here, we introduce an integrated platform that exploits gene expression compendia generated from drug-treated cell lines and primary tumor tissue to identify therapeutic candidates that can be used in the treatment of acute myeloid leukemia (AML). Our framework combines these data with patient survival information to identify potential candidates that presumably have a significant impact on AML patient survival. We use a drug regulatory score (DRS) to measure the similarity between drug-induced cell line and patient tumor gene expression profiles, and show that these computed scores are highly correlated with in vitro metrics of pharmacological activity. Furthermore, we conducted several in vivo validation experiments of our potential candidate drugs in AML mouse models to demonstrate the accuracy of our in silico predictions.

SUBMITTER: Ung MH

PROVIDER: S-EPMC5243857 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery.

Ung M H MH Sun C-H CH Weng C-W CW Huang C-C CC Lin C-C CC Liu C-C CC Cheng C C

The pharmacogenomics journal 20160315 4

Screening for drug compounds that exhibit therapeutic properties in the treatment of various diseases remains a challenge even after considerable advancements in biomedical research. Here, we introduce an integrated platform that exploits gene expression compendia generated from drug-treated cell lines and primary tumor tissue to identify therapeutic candidates that can be used in the treatment of acute myeloid leukemia (AML). Our framework combines these data with patient survival information t ...[more]

PMID: 26975228

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Project description:BackgroundBesides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level.MethodsHerein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug-target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug's mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets.ResultsWe receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions.ConclusionsTo the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

Dataset Information

Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery.

Publications

Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery.

Similar Datasets

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