Breast Cancer Cell Density: Imaging assay of the density- and context-dependence of small molecule perturbagen response in breast cancer cell lines. Dataset 3 of 3: Calculated dose-response metrics.
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ABSTRACT:
SUBMITTER: Marc Hafner, Mario Niepel
PROVIDER: LDS-1263 | LINCS | 2017-03-14
REPOSITORIES: LINCS
Dataset's files
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| HMS_LINCS&file=LDS-1263.tar.gz | Other |
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2017-03-14 | LDS-1262 | LINCS
Project description:In previous studies, we identified a distantly related rhomboid homologue gene known as RHBDD2 (Rhomboid domain containing 2) to be markedly overexpressed in the advanced stages of the breast and colorectal cancer diseases. In order to identify RHBDD2 modulated pathways, we analyzed two breast cancer cell lines (MCF7 and T47D) from control and RHBDD2-siRNA transient gene silencing followed by gene expression profiling analysis using the whole genome Toray 3D-GeneTM Human Oligo Chip. Statistical analysis of the Toray's 3D gene expression profiling data identified 566 commonly differentially expressed genes in association to the RHBDD2 knockdown in both breast cancer cell lines. Among the statistically significant over-represented biological process, we found the apoptosis, cell cycle and response to DNA damage process related genes. In addition, categories of genes found in the ubiquitin-proteasome and oxidative phosphorylation were also highly enriched related genes in the commonly deregulated gene list. We further used a lentivirus-based system (shRNA-pLKO.1) for stable silencing of RHBDD2 mRNA in the T47D breast cancer cell line. Using a staurosporine-induced apoptosis model, we demonstrate that RHBDD2 abrogation resulted in an apoptosis-resistant phenotype of T47D breast cancer cell line. These data are in line with a recent study, suggesting that RHBDD2 expression could be up-modulated in response to 5FU-induced apoptosis in colorectal cancer cells. Taken together, these data suggest that RHBDD2 could be involved in the modulation of the programmed cell death in cancer cells. In order to analyze differential gene expression profiling of RHBDD2 silencing and control cells, total RNA was isolated from replicate experiments from two breast cancer cell lines (MCF7 and T47D) derived from the negative control-siRNA and the RHBDD2-siRNA treatments in duplicate experiments.
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Project description:PurposeTo evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify small molecules that revert pathologic gene signature and alter disease phenotype in orbital adipose stem cells (OASCs) derived from patients with thyroid-associated orbitopathy (TAO).MethodsDifferentially expressed genes identified via RNA sequencing were inputted into LINCS L1000 Characteristic Direction Signature Search Engine (L1000CDS2) to predict candidate small molecules to reverse pathologic gene expression. TAO OASC cell lines were treated in vitro with six identified small molecules (Torin-2, PX12, withaferin A, isoliquiritigenin, mitoxantrone, and MLN8054), and expression of key adipogenic and differentially expressed genes was measured with quantitative polymerase chain reaction after 7 days of treatment. OASCs were differentiated into adipocytes, treated for 15 days, and stained with Oil Red O (OD 490 nm) to evaluate adipogenic changes.ResultsThe expression of key differentially expressed genes (IRX1, HOXB2, S100B, and KCNA4) and adipogenic genes (peroxisome proliferator activated receptor-γ, FABP4) was significantly decreased in TAO OASCs after treatment (P < .05). In treated TAO adipocytes (n = 3), all six tested small molecules yielded significant decrease (P < .05) in Oil Red O staining. In treated non-TAO adipocytes (n = 3), only three of the drugs yielded a significant decrease in Oil Red O staining.ConclusionsCombining disease expression signatures with LINCS small molecule prediction software can identify promising preclinical drug candidates for TAO.Translational relevanceThese findings may offer insight into future potential therapeutic options for TAO and demonstrate a streamlined model to predict drug candidates for other diseases.
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