Abstract
Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.
Highlights
The Library of Integrated Network-based Cellular Signatures (LINCS) program[8], funded by the Big Data to Knowledge (BD2K) Initiative at the National Institutes of Health (NIH), generated genetic and molecular signatures of human cell lines in response to various perturbations
We study the enrichment of the network modules with respect to Anatomical Therapeutic Chemical (ATC) classes
We first construct drug association networks (DANs) from different information corresponding to different characteristics of the LINCS data
Summary
The Library of Integrated Network-based Cellular Signatures (LINCS) program[8], (https://clue.io/), funded by the Big Data to Knowledge (BD2K) Initiative at the National Institutes of Health (NIH), generated genetic and molecular signatures of human cell lines in response to various perturbations. In[24] drug-target and drug-drug networks have been constructed based on the DrugBank database utilizing information about FDA approved and non-approved drugs and their corresponding targets Their analysis focused exclusively on drugs and compounds with known targets and did not take into consideration dynamic activity profiles as represented, e.g., by transcriptomics data. We introduce a method for constructing Drug Association Networks (DAN) based on almost two million gene expression profiles for over 20,000 chemical perturbagens and seventy-two human cell lines. In these networks nodes correspond to drugs and two drugs are connected if their profile responses are similar, as measured by the statistical significance of the Jaccard Index (JI).
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