Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes. Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes. Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia. Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk.

[1]  A. Tveito,et al.  Validating the Arrhythmogenic Potential of High-, Intermediate-, and Low-Risk Drugs in a Human-Induced Pluripotent Stem Cell-Derived Cardiac Microphysiological System. , 2022, ACS Pharmacology & Translational Science.

[2]  Evan W. Miller,et al.  Metabolically driven maturation of human-induced-pluripotent-stem-cell-derived cardiac microtissues on microfluidic chips , 2022, Nature Biomedical Engineering.

[3]  M. Mahajan,et al.  A library of induced pluripotent stem cells from clinically well-characterized, diverse healthy human individuals , 2020, bioRxiv.

[4]  Amanda J. Pickard,et al.  Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity , 2020, Nature Communications.

[5]  G. Norman,et al.  Limitations of Animal Studies for Predicting Toxicity in Clinical Trials: Is it Time to Rethink Our Current Approach? , 2019 .

[6]  Gail A. Van Norman,et al.  Limitations of Animal Studies for Predicting Toxicity in Clinical Trials , 2019, JACC. Basic to translational science.

[7]  G. Gintant,et al.  Use of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes in Preclinical Cancer Drug Cardiotoxicity Testing: A Scientific Statement From the American Heart Association. , 2019, Circulation research.

[8]  Karoline Horgmo Jæger,et al.  Improved Computational Identification of Drug Response Using Optical Measurements of Human Stem Cell Derived Cardiomyocytes in Microphysiological Systems , 2019, bioRxiv.

[9]  Sarah A. Boswell,et al.  Adaptation of Human iPSC-Derived Cardiomyocytes to Tyrosine Kinase Inhibitors Reduces Acute Cardiotoxicity via Metabolic Reprogramming. , 2019, Cell systems.

[10]  Daniel C Millard,et al.  International Multisite Study of Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes for Drug Proarrhythmic Potential Assessment , 2018, Cell reports.

[11]  A. Tveito,et al.  Inversion and computational maturation of drug response using human stem cell derived cardiomyocytes in microphysiological systems , 2018, bioRxiv.

[12]  W. Giles,et al.  Pro-arrhythmic effects of low plasma [K+] in human ventricle: An illustrated review. , 2017, Trends in cardiovascular medicine.

[13]  M. Birtwistle,et al.  Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics , 2017, Front. Physiol..

[14]  E. Sobie,et al.  Population-based mechanistic modeling allows for quantitative predictions of drug responses across cell types , 2017, bioRxiv.

[15]  Kevin R. DeMarco,et al.  A multiscale computational modelling approach predicts mechanisms of female sex risk in the setting of arousal‐induced arrhythmias , 2017, The Journal of physiology.

[16]  Zhilin Qu,et al.  Electrophysiology of Hypokalemia and Hyperkalemia. , 2017, Circulation. Arrhythmia and electrophysiology.

[17]  Praveen Shukla,et al.  High-throughput screening of tyrosine kinase inhibitor cardiotoxicity with human induced pluripotent stem cells , 2017, Science Translational Medicine.

[18]  R. Iyengar,et al.  A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries , 2017, bioRxiv.

[19]  I. Abraham,et al.  Comparative Effectiveness of Newer Tyrosine Kinase Inhibitors Versus Imatinib in the First-Line Treatment of Chronic-Phase Chronic Myeloid Leukemia Across Risk Groups: A Systematic Review and Meta-Analysis of Eight Randomized Trials. , 2016, Clinical lymphoma, myeloma & leukemia.

[20]  Russ B Altman,et al.  Human induced pluripotent stem cell–derived cardiomyocytes recapitulate the predilection of breast cancer patients to doxorubicin-induced cardiotoxicity , 2016, Nature Medicine.

[21]  H. Gharwan,et al.  Kinase inhibitors and monoclonal antibodies in oncology: clinical implications , 2016, Nature Reviews Clinical Oncology.

[22]  Thomas Streichert,et al.  Analysis of Tyrosine Kinase Inhibitor-Mediated Decline in Contractile Force in Rat Engineered Heart Tissue , 2016, PloS one.

[23]  T. Colatsky,et al.  A New Perspective in the Field of Cardiac Safety Testing through the Comprehensive In Vitro Proarrhythmia Assay Paradigm , 2016, Journal of biomolecular screening.

[24]  M. Ewer,et al.  Cardiotoxicity of anticancer treatments , 2015, Nature Reviews Cardiology.

[25]  A. Lindahl,et al.  Identification of novel biomarkers for doxorubicin-induced toxicity in human cardiomyocytes derived from pluripotent stem cells , 2015, Toxicology.

[26]  Kwang-Hyun Cho,et al.  The switching role of β-adrenergic receptor signalling in cell survival or death decision of cardiomyocytes , 2014, Nature Communications.

[27]  H. Geys,et al.  The concordance between nonclinical and phase I clinical cardiovascular assessment from a cross-company data sharing initiative. , 2014, Toxicological sciences : an official journal of the Society of Toxicology.

[28]  Nima Milani-Nejad,et al.  Small and large animal models in cardiac contraction research: advantages and disadvantages. , 2014, Pharmacology & therapeutics.

[29]  S. Severi,et al.  Computational Models of Ventricular- and Atrial-Like Human Induced Pluripotent Stem Cell Derived Cardiomyocytes , 2013, Annals of Biomedical Engineering.

[30]  Kevin A Janes,et al.  Models of signalling networks – what cell biologists can gain from them and give to them , 2013, Journal of Cell Science.

[31]  Gary R. Mirams,et al.  mRNA Expression Levels in Failing Human Hearts Predict Cellular Electrophysiological Remodeling: A Population-Based Simulation Study , 2013, PloS one.

[32]  M. Suematsu,et al.  Distinct metabolic flow enables large-scale purification of mouse and human pluripotent stem cell-derived cardiomyocytes. , 2013, Cell stem cell.

[33]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[34]  R. Hajjar,et al.  Altered sarcoplasmic reticulum calcium cycling—targets for heart failure therapy , 2012, Nature Reviews Cardiology.

[35]  Jeffrey J Saucerman,et al.  Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling* , 2012, The Journal of Biological Chemistry.

[36]  R. Iyengar,et al.  Merging Systems Biology with Pharmacodynamics , 2012, Science Translational Medicine.

[37]  Jochen H M Prehn,et al.  Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy , 2011, Gut.

[38]  Gordon Keller,et al.  SIRPA is a specific cell-surface marker for isolating cardiomyocytes derived from human pluripotent stem cells , 2011, Nature Biotechnology.

[39]  Ravi Iyengar,et al.  Systems Biology—Biomedical Modeling , 2011, Science Signaling.

[40]  Gordon Keller,et al.  Stage-specific optimization of activin/nodal and BMP signaling promotes cardiac differentiation of mouse and human pluripotent stem cell lines. , 2011, Cell stem cell.

[41]  K. Kolaja,et al.  Cardiotoxicity of kinase inhibitors: the prediction and translation of preclinical models to clinical outcomes , 2011, Nature Reviews Drug Discovery.

[42]  F. Vogenberg,et al.  Personalized medicine: part 1: evolution and development into theranostics. , 2010, P & T : a peer-reviewed journal for formulary management.

[43]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[44]  J. Rice,et al.  Approximate model of cooperative activation and crossbridge cycling in cardiac muscle using ordinary differential equations. , 2008, Biophysical journal.

[45]  Eric D. Adler,et al.  Human cardiovascular progenitor cells develop from a KDR+ embryonic-stem-cell-derived population , 2008, Nature.

[46]  R. Shah,et al.  Refining detection of drug-induced proarrhythmia: QT interval and TRIaD. , 2005, Heart rhythm.

[47]  T. Fleming,et al.  Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. , 2001, The New England journal of medicine.

[48]  Ennis,et al.  Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. , 2001, The New England journal of medicine.

[49]  I. Cohen,et al.  Tyrosine kinase inhibition reduces if in rabbit sinoatrial node myocytes , 1997, Pflügers Archiv.