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.

Figure 1

Analytic workflow. A) Two gene expression collections: a set of disease gene expression with corresponding controls and gene expression of tissue treated drugs and small molecules with corresponding controls. Significance Analysis of Microarrays (SAM) is used to obtain a signature of significantly up and down regulated genes for each disease. Rank normalization and the pre-processing procedure previously described is used to create a reference database of drug gene expression. B) A modification to the Connectivity Map method (25) is used to query the disease signature against the drug reference expression set to assign a drug-disease score to each drug-disease pair based on profile similarity. These scores are interpreted resulting in a list of candidate therapeutics for each disease of interest.

Figure 2

Heatmap of drug-disease scores. Most of the heatmap is white, indicating the majority of drug and gene expression profiles are not significantly concordant. Yellow indicates a negative (therapeutic) drug-disease score meaning the expression profiles of the two are opposing, and that the drug might be a potential treatment option of the disease. Blue indicates a positive drug-disease score meaning the expression profiles of the two are similar, and therefore the drug may be antagonistic towards the disease.

Figure 3

Hierarchical clustering of drugs and diseases by predicted therapeutic scores. A) Drugs are clustered based on their prediction scores. Several groups are highlighted in color representing clusters of drugs with known shared mechanisms of action. B) Diseases are clustered based on their prediction scores. Groups highlighted with color are known to share characteristic pathophysiology.

Figure 3

Hierarchical clustering of drugs and diseases by predicted therapeutic scores. A) Drugs are clustered based on their prediction scores. Several groups are highlighted in color representing clusters of drugs with known shared mechanisms of action. B) Diseases are clustered based on their prediction scores. Groups highlighted with color are known to share characteristic pathophysiology.

Figure 4

Experimental validation of cimetidine for lung adenocarcinoma. A) MTT calorimetric assay showing dose-dependent inhibition of lung adenocarcinoma cell growth after exposure to cimetidine in vitro. B) Evaluation of apoptosis by TUNEL assay. Lung adenocarcinoma cells treated with 2000 µM cimetidine exhibit a significant increase in TUNEL-positive (green) nuclei compared to vehicle (PBS) treated control. C) Results from a tumor xenograft experiment testing the efficacy of H2-agonist cimetidine in inhibiting the growth of A549 lung adenocarcinoma cell line tumors in SCID mice. Three treatment groups (25/50/100 mg/kg/injection) and one control group (PBS) was used. Another group was treated with doxorubicin as a positive control. Concordant with our prediction, the results demonstrate that cimetidine shows efficacy in inhibiting the growth of NSCLC xenograft implant tumors in a dose-dependent manner. D) Representative images of tumors treated with high dose of cimetidine (left) and control (right).