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 () and Ravi Iyengar ()
Additional contact information
J. G. Coen van Hasselt: Icahn School of Medicine at Mount Sinai
Rayees Rahman: Icahn School of Medicine at Mount Sinai
Jens Hansen: Icahn School of Medicine at Mount Sinai
Alan Stern: Icahn School of Medicine at Mount Sinai
Jaehee V. Shim: Icahn School of Medicine at Mount Sinai
Yuguang Xiong: Icahn School of Medicine at Mount Sinai
Amanda Pickard: Icahn School of Medicine at Mount Sinai
Gomathi Jayaraman: Icahn School of Medicine at Mount Sinai
Bin Hu: Icahn School of Medicine at Mount Sinai
Milind Mahajan: Icahn School of Medicine at Mount Sinai
James M. Gallo: Icahn School of Medicine at Mount Sinai
Joseph Goldfarb: Icahn School of Medicine at Mount Sinai
Eric A. Sobie: Icahn School of Medicine at Mount Sinai
Marc R. Birtwistle: Icahn School of Medicine at Mount Sinai
Avner Schlessinger: Icahn School of Medicine at Mount Sinai
Evren U. Azeloglu: Icahn School of Medicine at Mount Sinai
Ravi Iyengar: Icahn School of Medicine at Mount Sinai

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18396-7

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