Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptional profiling of LS1034 cells treated with tepoxalin

ABSTRACT: LS1034 colorectal cancer cells were treated with DMSO vehicle control or 12 uM tepoxalin for 6 hours. Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. To fully discover activity against multiple tumor types, resource-intensive screening across hundreds of cancer cell lines is needed. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves the molecular barcoding of each cell line, followed by pooling of the barcoded lines. Relative barcode abundance following drug treatment versus controls thus reflects cell viability following drug treatment. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Mechanistic follow-up of several of these compounds revealed novel mechanisms. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development.

ORGANISM(S): Homo sapiens

PROVIDER: GSE133286 | GEO | 2019/08/08

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Transcriptional profiling of SF295 cells following MTF1 knockout by CRISPR/Cas9

Project description:SF295 glioblastoma cells were subjected to CRISPR/Cas9-mediated knockout of the MTF1 (metal regulatory transcription factor 1) gene or non-targeted control (GFP). Two guides targeting MTF1 and one guide targeting GFP were cloned into the pXPR_023 all-in-one CRISPR/Cas9 vector. SF295 cells were transduced by lentiviral infection and selected using puromycin. Following selection, RNA was harvested from cell lines growing in culture. Wild-type SF295 cells were also included as a control. Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. To fully discover activity against multiple tumor types, resource-intensive screening across hundreds of cancer cell lines is needed. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves the molecular barcoding of each cell line, followed by pooling of the barcoded lines. Relative barcode abundance following drug treatment versus controls thus reflects cell viability following drug treatment. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Mechanistic follow-up of several of these compounds revealed novel mechanisms. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development.

2019-08-08 | GSE133285 | GEO

Discovering the anti-cancer potential of non-oncology drugs by systematic PRISM profiling

Project description:Discovering the anti-cancer potential of non-oncology drugs by systematic PRISM profiling

| PRJNA551012 | ENA

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics-2

Project description:Kinase inhibitors (KIs) are important cancer drugs but often display polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same cells against 150 cancer drugs. The resulting 2,550 phenotypic drug profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of kinase inhibitors and identified markers of drug sensitivity or resistance. All data is publically accessible via an interactive web application that enables exploration of this rich molecular resource for better understanding active signalling pathways in sarcoma cells, identifying treatment response predictors, and revealing novel MoA of clinical KIs.

2023-11-29 | MSV000093533 | MassIVE

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics

2023-01-11 | MSV000091043 | MassIVE

Pharmacoproteomics identifies kinase pathways that drive the epithelial-mesenchymal transition and drug resistance in hepatocellular carcinoma

Project description:Hepatocellular carcinoma (HCC) is a complex and deadly disease lacking druggable genetic mutations. The limited efficacy of systemic treatments for advanced HCC implies that novel predictive biomarkers and drug targets are urgently needed. Most HCC drugs target protein kinases and their kinase-dependent signaling networks to drive HCC progression. To identify HCC signaling networks that determine responses to kinase inhibitors (KIs), we apply a pharmacoproteomics approach integrating kinome activity in 17 HCC cell lines with their responses to 299 KIs, resulting in a comprehensive dataset of pathway-based drug response signatures. By profiling patient HCC samples, we identify signatures of clinical HCC drug responses in individual tumors. Our analyses reveal kinase networks promoting the epithelial-mesenchymal transition (EMT) and drug resistance, including a FZD2-AXL-NUAK1/2 signaling module, whose inhibition reverses the EMT and sensitizes HCC cells to drugs. Our approach identifies cancer drug targets and molecular signatures of drug response for personalized oncology.

2018-12-15 | MSV000083236 | MassIVE

Landscape of immune-related signatures induced by targeting of epigenetic regulators in melanoma III

Project description:Epigenetic drugs exert a wide range of immune-related effects, but have strong drug-specific heterogeneity in immunomodulation, thus hampering selection of the most promising agent for innovative cancer immunotherapy approaches. Here we identified immune-related signatures induced by four classes of epigenetic drugs in melanoma cells to define the most active agent and to understand its biological activity in -vitro, in a pre-clinical model and in clinical samples. Gene modulation, induced by inhibitors of DNA methyltransferases (guadecitabine), histone deacetylases (givinostat), bromodomain and extraterminal domain proteins (JQ1 and OTX-015) and enhancer of zeste homolog 2 (GSK126), was assessed in human melanoma cell lines. All drugs modulated genes belonging to 20 families. Guadecitabine, followed by givinostat, was the most active drug and upregulated >160 immune-related genes characterized by low expression and high methylation. JQ1 and OTX-015 showed predominant inhibitory effects, GSK126 was the least active. A dominant immunomodulatory effect of guadecitabine and JQ1 was observed in combinatorial treatments. This experiment deals with the characterization of gene expression in tumor nodules removed from immunodeficient mice treated or not with guadecitabine (as described in Covre A et al. Seminars in Oncology 2015;42:506-513) after s.c. tranplant of human melanoma cells from two cell lines 313 and 195 (cell lines described in Coral S et al. J. Cell Physiol 2006;207:58-66).

2022-11-23 | GSE189630 | GEO

Anticancer properties of distinct antimalaria drug classes

Project description:Background: Antimalarials have anticancer potential. Results: We have systematically tested five distinct antimalaria drugs in a panel of cancer cell lines. Conclusion: Three antimalarial classes display potent antiproliferative activity, and their potency is correlated with cancer cell gene expression patterns. Significance: We confirm and extend anticancer potential of these antimalarials and we discuss their therapeutic potential based on clinical data. Key words: Malaria, Anticancer drug, Drug Discovery, Genomics, Gene Expression, Bioinformatics, Cancer AffymetrixM-BM-. HG-U133 Plus 2.0 mRNA expression arrays were used to determine the expression. CEL result files were pre-processed using the RMA (Irizarry et al, 2003) algorithm. This microarray analysis was performed for 91 cell lines.

2013-10-26 | E-GEOD-51739 | biostudies-arrayexpress

Discovery of a drug targeting microenvironmental support for lymphoma cells by screening using patient-derived xenograft cells

Project description:Cell lines have been used for drug discovery as useful models of cancers; however, they do not recapitulate cancers faithfully, especially in the points of rapid growth rate and microenvironment independency. Consequently, the majority of conventional anti-cancer drugs are less sensitive to slow growing cells and do not target microenvironmental support, although most primary cancer cells grow slower than cell lines and depend on microenvironmental support. Here, we developed a novel high throughput drug screening system using patient-derived xenograft (PDX) cells of lymphoma that maintained primary cancer cell phenotype more than cell lines. The library containing 2613 known pharmacologically active substance and off-patent drugs were screened by this system. We could find many compounds showing higher cytotoxicity than conventional anti-tumor drugs. Especially, pyruvinium pamoate showed the highest activity, and its strong anti-tumor effect was confirmed also in vivo. We extensively investigated its mechanism of action and found that it inhibited glutathione supply from stromal cells to lymphoma cells, implying the importance of the stromal protection from ox 1 idative stress for lymphoma cell survival and a new therapeutic strategy for lymphoma. Our system introduces a primary cancer cell phenotype into cell-based phenotype screening and sheds new light on anti-cancer drug development. Global gene expression profiles of PDX cells showed high similarity to those of original primary cells. The correlation coefficient of gene expression profiles between PDX cells and the originalprimary cells was 0.814-0.890.

2015-08-31 | E-GEOD-71944 | biostudies-arrayexpress

Genetic dependencies associated with transcription factor activities in human cancer cell lines [CROP-seq]

Project description:Transcription factors (TFs) are important mediators of aberrant transcriptional programs in cancer cells. In this study, we focused on TF activity (TFa) as a new biomarker for cell line-selective anti-proliferative effects, in that high TFa predicts sensitivity to loss-of-function of a given gene (i.e., genetic dependencies (GD)). Our linear regression-based framework identified 3,047 pan-cancer and 3,952 cancer-typespecific candidate TFa-GD associations from cell line data, which were then crossexamined for impact on survival in patient cohorts. One of the most prominent biomarkers was TEAD1 activity, whose associations with its predicted GDs were validated through experimental evidence as proof of concept. Overall, these TFa-GD associations represent an attractive resource for identifying innovative, biomarkerdriven hypotheses for drug discovery programs in oncology.

2024-04-29 | GSE263747 | GEO

Drug-induced change in gene expression across NCI-60 cell lines after exposure to 15 anticancer agents for 2, 6 and 24h (sunitinib)

Project description:To identify patterns of drug-induced gene modulation that occur across different cell types, we measured gene expression changes across NCI-60 cell lines after exposure to 15 anticancer agents. The results were integrated into a database and set of interactive analysis tools, the NCI Transcriptional Pharmacodynamics Workbench (NCI TPW), intended to allow exploration of gene expression modulation, including by molecular pathway, drug target, and association with drug sensitivity. We identified common transcriptional responses across drugs and cell types and uncovered cell signaling pathway–specific gene expression changes associated with drug sensitivity. We also demonstrated the value of this tool for investigating clinically relevant molecular hypotheses, utilizing the NCI TPW to assess drug-induced expression changes in genes associated with immune function and epithelial-mesenchymal transition, and to identify candidate biomarkers for drug activity. The NCI TPW provides a comprehensive resource to facilitate understanding of tumor cell characteristics that define sensitivity to anticancer drugs.

2018-10-09 | GSE116449 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data