Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity

ABSTRACT: Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity

PROVIDER: PRJNA609277 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR11197100.fastq.gz	Fastqsanger.gz
	SRR11197200.fastq.gz	Fastqsanger.gz
	SRR11197300.fastq.gz	Fastqsanger.gz
	SRR11197400.fastq.gz	Fastqsanger.gz
	SRR11197500.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 572

Similar Datasets

High-Throughput Screening of Human Induced Pluripotent Stem Cell Cardiomyocytes Predicts Tyrosine Kinase Inhibitor Cardiotoxicity

Project description:High-Throughput Screening of Human Induced Pluripotent Stem Cell Cardiomyocytes Predicts Tyrosine Kinase Inhibitor Cardiotoxicity

2016-10-20 | GSE88944 | GEO

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity [DGE]

Project description:Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity [DGE]

| PRJNA609281 | ENA

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity [Conv]

Project description:Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity [Conv]

| PRJNA609282 | ENA

Tyrosine Kinase Inhibitor Cardiotoxicity

Project description:Tyrosine Kinase Inhibitor Cardiotoxicity

| PRJNA352077 | ENA

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity [DGE]

Project description:Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood and its prediction remains challenging. Here, we perform transcriptomic profiling of human heart-derived primary cardiomyocyte cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.

2020-05-13 | GSE146096 | GEO

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity [Conv]

2020-05-13 | GSE146095 | GEO

High-Throughput Screening of Tyrosine Kinase Inhibitor Cardiotoxicity using Human Induced Pluripotent Stem Cells

Project description:Tyrosine kinase inhibitors (TKIs), despite efficacy as anti-cancer therapies, are associated with cardiovascular side effects ranging from induced arrhythmias to heart failure. We have utilized patient-specific human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), generated from 11 healthy individuals and 2 patients receiving cancer treatment, to screen FDA-approved TKIs for cardiotoxicities by measuring alterations in cardiomyocyte viability, contractility, electrophysiology, calcium handling, and signaling. With these data, we generated a “cardiac safety index” to assess cardiotoxicities of existing TKIs. Many TKIs with a low cardiac safety index exhibit cardiotoxicity in patients. We also derived endothelial cells (hiPSC-ECs) and cardiac fibroblasts (hiPSC-CFs) to examine cell type-specific cardiotoxicities. Using high-throughput screening, we determined that VEGFR2/PDGFR-inhibiting TKIs caused cardiotoxicity in hiPSC-CMs, hiPSC-ECs, and hiPSC-CFs. Using phosphoprotein analysis, we determined that VEGFR2/PDGFR-inhibiting TKIs led to a compensatory increase in cardioprotective insulin and insulin-like growth factor (IGF) signaling in hiPSC-CMs. Activating cardioprotective signaling with exogenous insulin or IGF1 improved hiPSC-CM viability during co-treatment with cardiotoxic VEGFR2/PDGFR-inhibiting TKIs. Thus, hiPSC-CMs can be used to screen for cardiovascular toxicities associated with anti-cancer TKIs, correlating with clinical phenotypes. This approach provides unexpected insights, as illustrated by our finding that toxicity can be alleviated via cardioprotective insulin/IGF signaling.

2016-11-02 | GSE89410 | GEO

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity

Project description:Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is an integral part of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that cab predict adverse effects, especially cardiotoxicity, of drugs approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (308 cell line/drug combinations + 64 HeLa control lysates + 9 auxiliary treatment samples) have been conducted at the Center for Advanced Proteomics Research at Rutgers-New Jersey Medical School. The integrated proteomic and transcriptomic signatures have been used for computational network analysis to identify cellular signatures of cardiotoxicity that may predict drug-induced toxicity and possible mitigation of such toxicities by mixing different drugs. Both raw and processed proteomics data have been carefully controlled for quality and have been made publicly available via the PRoteomics IDEntifications (PRIDE) database. As such, this broad drug-stimulated proteomic dataset is valuable for the prediction drug toxicities and their mitigation.

2021-10-18 | PXD014791 | Pride

Kinome and transcriptome profiling reveal broad and distinct activities of erlotinib, sunitinib, and sorafenib in the mouse heart and suggest cardiotoxicity from combined STAT and EGFR inhibition

Project description:We administered clinically relevant doses of sorafenib, sunitinib (cardiotoxic multi-targeted kinase inhibitors) or erlotinib (a cardiosafe EGFR inhibitor) to mice daily for two weeks. We then compared the effects of these three kinase inhibitors on the cardiac transcriptome using RNAseq and the cardiac kinome using multiplexed inhibitor beads coupled with mass spectrometry. We found unexpectedly broad molecular effects of all three kinase inhibitors, suggesting that target kinase selectivity does not define either the molecular response or the potential for cardiotoxicity. Using in vivo drug administration and primary cardiomyocyte culture, we also show that the cardiosafety of erlotinib treatment may result from upregulation of the cardioprotective STAT3 pathway, as co-treatment with erlotinib and a STAT inhibitor decreases cardiac contractile function and cardiomyocyte fatty acid oxidation.

2018-02-13 | GSE98973 | GEO

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity.

Project description:Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood, and its prediction remains challenging. We obtain transcriptional profiles of human heart-derived primary cardiomyocyte like cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with the cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.

| S-EPMC7511315 | biostudies-literature

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