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

J. G.Coen van Hasselt, Rayees Rahman, Jens Hansen, Alan Stern, Jaehee V. Shim, Yuguang Xiong, Amanda Pickard, Gomathi Jayaraman, Bin Hu, Milind Mahajan, James M. Gallo, Joseph Goldfarb, Eric A. Sobie, Marc R. Birtwistle, Avner Schlessinger, Evren U. Azeloglu, Ravi Iyengar

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish
Article number4809
JournalNature Communications
Issue number1
StatePublished - 1 Dec 2020


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