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Discovery and preclinical validation of drug indications using compendia of public gene expression data.

ABSTRACT: The application of established drug compounds to new therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning use high-throughput experimental approaches to assess a compound's potential therapeutic qualities. Here, we present a systematic computational approach to predict novel therapeutic indications on the basis of comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds, yielding predicted therapeutic potentials for these drugs. We recovered many known drug and disease relationships using computationally derived therapeutic potentials and also predict many new indications for these 164 drugs. We experimentally validated a prediction for the antiulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate its efficacy both in vitro and in vivo using mouse xenograft models. This computational method provides a systematic approach for repositioning established drugs to treat a wide range of human diseases.

SUBMITTER: Sirota M

PROVIDER: S-EPMC3502016 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Discovery and preclinical validation of drug indications using compendia of public gene expression data.

Sirota Marina M Dudley Joel T JT Kim Jeewon J Chiang Annie P AP Morgan Alex A AA Sweet-Cordero Alejandro A Sage Julien J Butte Atul J AJ

Science translational medicine 20110801 96

The application of established drug compounds to new therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning use high-throughput experimental approaches to assess a compound's potential therapeutic qualities. Here, we present a systematic computational approach to predict novel therapeutic indications on the basis of comprehensive t ...[more]

PMID: 21849665

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Project description:OBJECTIVE:In response to a need for better sepsis diagnostics, several new gene expression classifiers have been recently published, including the 11-gene "Sepsis MetaScore," the "FAIM3-to-PLAC8" ratio, and the Septicyte Lab. We performed a systematic search for publicly available gene expression data in sepsis and tested each gene expression classifier in all included datasets. We also created a public repository of sepsis gene expression data to encourage their future reuse. DATA SOURCES:We searched National Institutes of Health Gene Expression Omnibus and EBI ArrayExpress for human gene expression microarray datasets. We also included the Glue Grant trauma gene expression cohorts. STUDY SELECTION:We selected clinical, time-matched, whole blood studies of sepsis and acute infections as compared to healthy and/or noninfectious inflammation patients. We identified 39 datasets composed of 3,241 samples from 2,604 patients. DATA EXTRACTION:All data were renormalized from raw data, when available, using consistent methods. DATA SYNTHESIS:Mean validation areas under the receiver operating characteristic curve for discriminating septic patients from patients with noninfectious inflammation for the Sepsis MetaScore, the FAIM3-to-PLAC8 ratio, and the Septicyte Lab were 0.82 (range, 0.73-0.89), 0.78 (range, 0.49-0.96), and 0.73 (range, 0.44-0.90), respectively. Paired-sample t tests of validation datasets showed no significant differences in area under the receiver operating characteristic curves. Mean validation area under the receiver operating characteristic curves for discriminating infected patients from healthy controls for the Sepsis MetaScore, FAIM3-to-PLAC8 ratio, and Septicyte Lab were 0.97 (range, 0.85-1.0), 0.94 (range, 0.65-1.0), and 0.71 (range, 0.24-1.0), respectively. There were few significant differences in any diagnostics due to pathogen type. CONCLUSIONS:The three diagnostics do not show significant differences in overall ability to distinguish noninfectious systemic inflammatory response syndrome from sepsis, though the performance in some datasets was low (area under the receiver operating characteristic curve, < 0.7) for the FAIM3-to-PLAC8 ratio and Septicyte Lab. The Septicyte Lab also demonstrated significantly worse performance in discriminating infections as compared to healthy controls. Overall, public gene expression data are a useful tool for benchmarking gene expression diagnostics.

Project description:BACKGROUND: Compendia of gene expression profiles under chemical and genetic perturbations constitute an invaluable resource from a systems biology perspective. However, the perturbational nature of such data imposes specific challenges on the computational methods used to analyze them. In particular, traditional clustering algorithms have difficulties in handling one of the prominent features of perturbational compendia, namely partial coexpression relationships between genes. Biclustering methods on the other hand are specifically designed to capture such partial coexpression patterns, but they show a variety of other drawbacks. For instance, some biclustering methods are less suited to identify overlapping biclusters, while others generate highly redundant biclusters. Also, none of the existing biclustering tools takes advantage of the staple of perturbational expression data analysis: the identification of differentially expressed genes. RESULTS: We introduce a novel method, called ENIGMA, that addresses some of these issues. ENIGMA leverages differential expression analysis results to extract expression modules from perturbational gene expression data. The core parameters of the ENIGMA clustering procedure are automatically optimized to reduce the redundancy between modules. In contrast to the biclusters produced by most other methods, ENIGMA modules may show internal substructure, i.e. subsets of genes with distinct but significantly related expression patterns. The grouping of these (often functionally) related patterns in one module greatly aids in the biological interpretation of the data. We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account. Finally, we apply ENIGMA to the Rosetta compendium of expression profiles for Saccharomyces cerevisiae and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions. CONCLUSION: It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.

Dataset Information

Discovery and preclinical validation of drug indications using compendia of public gene expression data.

Publications

Discovery and preclinical validation of drug indications using compendia of public gene expression data.

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