Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focused on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced pluripotent stem cell-derived cardiomyocytes. Using bulk transcriptomics profiles, we applied singular value decomposition to identify drug-selective patterns in cell lines obtained from multiple healthy human subjects. Cellular pathways affected by highly cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Whole genome sequences of the cell lines, using outlier responses enabled us to correctly reidentify a genomic variant associated with anthracycline cardiotoxicity and predict genomic variants potentially associated with TKI cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale predictive understanding of cardiotoxicity for drug development and patient stratification.

中文翻译：

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心脏毒性药物转录组特征的多尺度映射

药物诱导的基因表达谱可以识别潜在的毒性机制。我们专注于获得 FDA 批准的酪氨酸激酶抑制剂 (TKI) 在人诱导多能干细胞衍生的心肌细胞中的心脏毒性特征。使用批量转录组学概况，我们应用奇异值分解来识别从多个健康人类受试者获得的细胞系中的药物选择性模式。受高心脏毒性 TKI 影响的细胞通路包括能量代谢、收缩和细胞外基质动力学。将这些途径投射到单细胞表达谱表明可以在心肌细胞和成纤维细胞中诱发 TKI 反应。细胞系的全基因组序列，使用离群值反应使我们能够正确地重新识别与蒽环类药物心脏毒性相关的基因组变异，并预测可能与 TKI 心脏毒性相关的基因组变异。我们得出结论，当与公开可用的基因组、通路和单细胞转录组数据集整合时，mRNA 表达谱可为药物开发和患者分层提供对心脏毒性的多尺度预测理解。