Dataset Information

Cell-specific prediction and application of drug-induced gene expression profiles.

ABSTRACT: Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

SUBMITTER: Hodos R

PROVIDER: S-EPMC5753597 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Cell-specific prediction and application of drug-induced gene expression profiles.

Hodos Rachel R Zhang Ping P Lee Hao-Chih HC Duan Qiaonan Q Wang Zichen Z Clark Neil R NR Ma'ayan Avi A Wang Fei F Kidd Brian B Hu Jianying J Sontag David D Dudley Joel J

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20180101

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence ...[more]

PMID: 29218867

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Project description:ObjectivesGlioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures.MethodsWe first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings.ResultsWe identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset.ConclusionsIn this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.

Project description:BackgroundBatch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.ResultsDifferences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components.ConclusionBatch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

Dataset Information

Cell-specific prediction and application of drug-induced gene expression profiles.

Publications

Cell-specific prediction and application of drug-induced gene expression profiles.

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