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Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity
J G Coen van Hasselt1,2, Rayees Rahman1, Jens Hansen1
1Department of Pharmacological Sciences and Systems Biology Center New York, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Abstract:
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.
Insights
Predicting cardiotoxicity from kinase inhibitors (KIs) is challenging. This study found that acute transcriptomic changes in cell assays and drug structures can predict KI-induced heart damage, aiding future drug discovery.
Area of Science:
- Pharmacology
- Cardiology
- Genomics
Background:
- Kinase inhibitors (KIs) are vital anti-cancer drugs.
- Cardiotoxicity is a significant, poorly understood adverse event of KIs.
- Predicting KI cardiotoxicity remains a challenge.
Purpose of the Study:
- To identify predictive biomarkers for kinase inhibitor-induced cardiotoxicity.
- To understand the mechanisms underlying KI cardiotoxicity.
- To develop a predictive model for cardiotoxicity risk.
Main Methods:
- Transcriptional profiling of cardiomyocytes treated with 26 FDA-approved KIs.
- Analysis of FDA Adverse Event Reporting System data for cardiotoxicity risk scores.
- Elastic net regression to integrate transcriptomic data and clinical risk scores.
- Identification of gene signatures and relationships with KI structural profiles.
Main Results:
- A predictive gene signature for cardiotoxicity risk was identified.
- Acute transcriptomic changes in cell-based assays correlate with cardiotoxicity.
- Relationships between cardiotoxicity risk and KI structural/binding profiles were established.
Conclusions:
- Cell-based transcriptomic assays combined with drug substructures can predict KI-induced cardiotoxicity.
- This approach can inform the development of safer kinase inhibitors.
- Findings provide insights into mechanisms of KI cardiotoxicity for future drug discovery.
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