Ponatinib, a third-generation BCR::ABL1 inhibitor, has antileukemic activity but is associated with cardiovascular toxicity, for which transcriptome-level responses remain incompletely characterized. Here, we defined a ponatinib-associated transcriptomic signature and examined its mechanistic implications using two public RNA sequencing (RNA-Seq) datasets: GSE186341 (11 cancer cell lines treated with kinase inhibitors) and GSE217421 (induced pluripotent stem cell (iPSC)-derived cardiomyocytes treated with approved drugs). Principal component analysis (PCA) and k-means clustering were used to define expression-based subgroups of vehicle-treated (DMSO) controls. DESeq2, followed by fixed-effect meta-analysis, estimated subgroup-specific treatment effects and pooled effect estimates across subgroups. In GSE186341, we identified 2,639 meta-analytic differentially expressed genes (meta-DEGs). Among these, 81 genes were also differentially expressed in GSE217421 after ponatinib treatment, identifying an overlapping gene set across datasets. In contrast, imatinib showed no overlap with these 81 genes under the same cross-dataset analysis framework. Cardiotoxicity-relevant functions were represented by directionally consistent genes linked to cardiac repolarization-associated ion handling (KCNN3), insulin-responsive metabolic regulation (FOXO1, HK2), cyclic adenosine monophosphate (cAMP)-responsive stress signaling (RAPGEF3), and mitochondrial homeostasis and redox regulation (MCL1, GCH1). Collectively, these results define a ponatinib-associated transcriptomic signature and nominate cross-dataset transcript-level candidates for subsequent mechanistic and experimental validation in ponatinib-associated cardiotoxicity.
Kong et al. (Thu,) studied this question.
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