Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

High-throughput expression profiling of OCI-Ly3 cell line upon treatment with a panel of 14 known drugs.

ABSTRACT: Analysis of Diffuse Large B-Cell Lymphoma (DLBCL) OCI-LY3 cell line treated with 14 different known drugs at 2 different concentrations and profiled at 6, 12 and 24 hrs after treatment. We used this gene expression data to design a challenge where participants were required to develop methods to predict activity of compound pairs using gene expression profile following single compound treatment, drug response curve of each compound and baseline genetic profile of OCI-LY3 cell line. GeneChip HT HG-U219 array plates were used to analyze gene expression changes induced by treatment with 14 drug compounds in OCI-Ly3 cells at 6, 12 and 24h. 3 replicates were analyxed for each drug/time combination. Vehicle (DMSO)-treated control samples were used for comparison with each replicate. Altogether 282 samples were analyzed in this study.

ORGANISM(S): Homo sapiens

SUBMITTER: Mukesh Bansal

PROVIDER: E-GEOD-51068 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Shared Molecules

Only show the datasets with similarity scores above: 0.5

Threshold

0.5

Similar Datasets

Project description:Identifying the Mechanism of Action (MoA) of drugs is critical for the development of new drugs, understanding their side effects, and drug repositioning. However, identifying drug MoA has been challenging and has been traditionally attempted only though large experimental setups with little success. While advances in computational power offers the opportunity to achieve this in-silico, methods to exploit existing computational resources are still in their infancy. To overcome this, we developed a novel method to identify Drug Mechanism of Action using Network Dysregulation (DeMAND). The method is based on the realization that drugs affect the protein activity of their targets, but not necessarily their mRNA expression levels. In contrast, the change in protein activity directly affects the mRNA expression levels of downstream genes. Based on this hypothesis, DeMAND identifies drug MoA by comparing gene expression profiles following drug perturbation with control samples, and computing the change in the individual interactions within a pre-determined integrated transcriptional and post-translational regulatory model (interactome). This dataset includes GEPs in 3 different B-cell lymphoma cell lines (OCI-LY3, OCI-LY7 and U2932) at 6, 12, and 24hrs. 92 FDA approved compounds were used at a concentration of IC20 at 24h. DMSO was used as control at each time-point. A total of 828 samples and 29 control samples were available for analysis. Total RNA was isolated with the RNAqueous-96 Automated Kit (Ambion) on the Janus automated liquid handling system (Perkin Elmer Inc.), quantified by NanoDrop 6000 spectrophotometer and quality checked by Agilent Bioanalyzer. 300ng of each of the samples with RIN value >7 were converted to biotinylated cRNA with the Illumina TotalPrep-96 RNA Amplification Kit (Ambion) using a standard T7-based amplification protocol and hybridized on the Human Genome U219 96-Array Plate (Affymetrix). Hybridization, washing, staining and scanning of the array plates were performed on the GeneTitan Instrument (Affymetrix) according to manufacturer’s protocols.