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

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

PROVIDER: PRJNA609281 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11197200.fastq.gz	Fastqsanger.gz
	SRR11197300.fastq.gz	Fastqsanger.gz
	SRR11197400.fastq.gz	Fastqsanger.gz
	SRR11197500.fastq.gz	Fastqsanger.gz
	SRR11197600.fastq.gz	Fastqsanger.gz

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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.

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

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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.

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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.

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

Project description:This SuperSeries is composed of the SubSeries listed below.

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